ZLWB2
Tao Chen & Eva Brinkman
23 March 2018
#which libraries are needed
library("plyr")
library(deSolve)
library(minpack.lm)
library(FME)## Loading required package: rootSolve## Loading required package: codalibrary("Biostrings")## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
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## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
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## IQR, mad, xtabs## The following objects are masked from 'package:base':
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## anyDuplicated, append, as.data.frame, cbind, colnames,
## do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, lengths, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff,
## sort, table, tapply, union, unique, unsplit, which, which.max,
## which.min## Loading required package: S4Vectors## Loading required package: stats4##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:plyr':
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## rename## The following objects are masked from 'package:base':
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## colMeans, colSums, expand.grid, rowMeans, rowSums## Loading required package: IRanges##
## Attaching package: 'IRanges'## The following object is masked from 'package:plyr':
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## desc## Loading required package: XVector##
## Attaching package: 'XVector'## The following object is masked from 'package:plyr':
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## compactlibrary(truncnorm)
library(colorspace)
time_all <- c(0, 4, 8, 10, 12, 14, 16, 18, 21, 24, 33, 37, 38, 42,48,54, 60) #time point + shieldDeclaration of simple fitting functions
simpleDefault: for individual series comparing with ZLWB2fitDefault
simplePiledupOMMerge: for individual series comparing with ZLWB2PiledupOMMerge
##################################################################################
# simpleDefault: for individual series comparing with ZLWB2fitDefault
##################################################################################
simpleDefault = function(time,wt,indel){
t= seq(0,max(time*10))/10;
# fitting untransfected
x=time
y=wt/100
z=indel/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U = 1-coef(fitmodel)[1]
untransf=U
listkinetc=list(NULL);
rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]
km=parms[3]
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
return(c((U+P)/(U+P+M)-df[,2]))
}
# function for repair results
rxnrate_repair = function(parms,U){
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
return(list(1-P-U-M,(P+U)/(P+U+M),M/(P+U+M)))
}
# fitting
df= data.frame(time=x, wt=y*100, indel=z*100)
df[,1]=df[,1]-df[1,1]
df[,2:3]=df[,2:3]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8,8)
FL= try(modFit(f=rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
if (length(FL)>1){
residuals=resid (FL)
listkinetc=list(perfectF = 1/FL$par[2],
hlc = round(FL$par[1],1),
hlm = round(FL$par[3],1),
intact=rxnrate_repair(FL$par,U)[[2]],
broken=rxnrate_repair(FL$par,U)[[1]],
maxBro = max(rxnrate_repair(FL$par,U)[[1]]),
peakBro =time[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant=rxnrate_repair(FL$par,U)[[3]],
residual_sq=sum(residuals^2));
}
return(listkinetc);
}
##################################################################################
##################################################################################
# simplePiledupOMMerge: for individual series comparing with ZLWB2PiledupOMMerge
##################################################################################
simplePiledupOMMerge = function(time,wt,indel){
t= seq(0,max(time*10))/10;
rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]
km=parms[3]
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"]
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
rxnrate_fit = function(parms){
res = rep(list(NULL),ncol(wt));
for (i in 1:length(res)){
# fitting untransfected
x=time
y=wt[,i]/100
z=indel[,i]/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U = 1-coef(fitmodel)[1]
untransf=U;
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2]
km=log(2)/parms[3];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
res[[i]] =c(M/(U+P+M)-z)
}
return(unlist(res))
}
parms=c(8,8,8)
FL= try(modFit(f=rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
return(FL);
}Simulations
Try out various set of rate constants to investigate the shape of the curve
t= seq(0,max(time_all*10))/10;
####simple model functions
rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]
km=parms[3]
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for repair results
rxnrate_repair = function(parms,U){
kc=parms[1];
kp=parms[2];
km=parms[3];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[,1];
M= s[,3];
return(list(1-P-U-M,(P+U)/(P+U+M),M/(P+U+M)))
}
###test various rate constants
parms1 <- c(0.1, 0.1, 0.1)
parms2 <- c(0.1, 0.01, 0.1)
parms3 <- c(0.1, 0.1, 0.01)
parms4 <- c(0.01, 0.1, 0.1)
parms5 <- c(0.5, 0.1, 0.1)
parms6 <- c(0.5, 0.1, 0.01)
# plot intact
plot(main=paste('simple'),cex.main=1.5, t,100* rxnrate_repair(parms1,0)[[2]],lwd=2,lty=1, pch=16,cex=1.5,col=2,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='intact (%)', ylim=c(0,100), type="l")
lines(t,100* rxnrate_repair(parms2,0)[[2]],lwd=2,lty=2, col=2, type="l")
lines(t,100* rxnrate_repair(parms3,0)[[2]],lwd=2,lty=3, col=2, type="l")
lines(t,100* rxnrate_repair(parms4,0)[[2]],lwd=2,lty=4, col=2, type="l")
lines(t,100* rxnrate_repair(parms5,0)[[2]],lwd=2,lty=6, col=2, type="l")
lines(t,100* rxnrate_repair(parms6,0)[[2]],lwd=2,lty=5, col=2, type="l")
# plot mutated
plot(main=paste('simple'),cex.main=1.5, t,100*rxnrate_repair(parms1,0)[[3]],lwd=2,lty=1, pch=16,cex=1.5,col=3,cex.lab=1.5,cex.axis=1.5, xlab='time (h)',ylab='indel (%)', ylim=c(0,100), type="l")
lines(t,100* rxnrate_repair(parms2,0)[[3]],lwd=2,lty=2, col=3, type="l")
lines(t,100* rxnrate_repair(parms3,0)[[3]],lwd=2,lty=3, col=3, type="l")
lines(t,100* rxnrate_repair(parms4,0)[[3]],lwd=2,lty=4, col=3, type="l")
lines(t,100* rxnrate_repair(parms5,0)[[3]],lwd=2,lty=6, col=3, type="l")
lines(t,100* rxnrate_repair(parms6,0)[[3]],lwd=2,lty=5, col=3, type="l")
# plot broken fractions
plot(main=paste('simple'),cex.main=1.5,cex.lab=1.5,cex.axis=1.5, lwd=2,lty=1, t,c(rxnrate_repair(parms1,0)[[1]]*100),pch=16,type='l',cex=1.5,col=4,xlab='time (h)',ylab='broken (%)', ylim=c(0,100))
lines(t,100* rxnrate_repair(parms2,0)[[1]],lwd=2,lty=2, col=4, type="l")
lines(t,100* rxnrate_repair(parms3,0)[[1]],lwd=2,lty=3, col=4, type="l")
lines(t,100* rxnrate_repair(parms4,0)[[1]],lwd=2,lty=4, col=4, type="l")
lines(t,100* rxnrate_repair(parms5,0)[[1]],lwd=2,lty=6, col=4, type="l")
lines(t,100* rxnrate_repair(parms6,0)[[1]],lwd=2,lty=5, col=4, type="l")
rates <- data.frame(rates <- c('kc', 'kp', 'km'),
p1 <- round(parms1,2),
p2 <- round(parms2,2),
p3 <- round(parms3,2),
p4 <- round(parms4,2),
p5 <- round(parms5,2),
p6 <- round(parms6,2))
legend('topright',ncol=3, cex=1.0,bty='n', lty=c(NA, 1,2,3,4,6,5), t(rates))
Western blot based ODE model
1: Cas9 has zero activities (extremey large decay rate) without Shield-1
2: Cas9 is activated to certain stable form that has nuclease activity by adding Shield-1
3: Cas9 activity is proportional to Cas9 abundance quantified by western blot after introduction of Shield-1
4: Untransfected fraction (U) is estimated by sigmoid fitting
5: M(i)/(U+P+M) is the target of fitting, with M(i) being individual mutation or total mutation
6: Sweeping kp/km and apply F-statistic to estimate the upperbound of perfect repair versus mutagentic repair ratio
Cas9 kinetics
fitting to western blot data of Cas9 activation
library(FME);
# Cas9 protein kinetics
#average intensity of western blot (n=3)
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513, 1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
#individual intensities of separate experiments
tWB1 = c(60, 60, 60, 42, 42,42,33,33,33,24,24,24,21,21,21,18,18,18,16,16,16,14,14,14,12,12,12,10,10,10,8,8,8,4,4,4,0,0,0)
Cas9I1 = c(0.874859076, 0.928020566, 1.302469136, 0.722660654, 1.107969152, 0.962962963, 0.91319053, 1.03470437, 1.456790123, 0.735062007, 1.101542416, 1.104938272, 1.041713641, 0.61311054, 1.314814815, 0.862457723, 0.651670951, 1.197530864, 1.240135287, 0.597686375, 1.796296296, 0.426155581, 0.424164524, 0.87654321, 0.314543405, 0.686375321, 1.345679012, 0.562570462, 0.343187661, 1.154320988, 0.400225479,0.485861183, 0.938271605, 0.162344983,0.254498715, 0.559876543, 0.06833713, 0.262210797,0.098214286)
#Cas9 activity fitting
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
b=becy[1];
Dp=becy[2];
#overlay constant cas9 kinetics with WB data
plot(tWB1, Cas9I1/(becy[1]/becy[2])*100,pch=16, ylim=c(0,100),ylab='Cas9 activity (%)', xlab = 'time (h)',cex=1.5)
abline(h=100,lwd=3,col=4)
legend('bottomright',c('n = 3', ''),bty='n')
#overlay constant cas9 kinetics with fitting
plot(tWB1, Cas9I1/(becy[1]/becy[2])*100,pch=16, ylim=c(0,100),ylab='Cas9 activity (%)', xlab = 'time (h)',cex=1.5)
lines(seq(0,60,0.01), (b/Dp+(-b/Dp)*exp(-Dp*seq(0,60,0.01)))/(becy[1]/becy[2])*100,lwd=3)
legend('bottomright',c('n = 3', paste('+shield =',round(log(2)/becy[2],1),'h'),''),bty='n')
#overlay cas9 fitting with average WB data
plot(tWB, Cas9I/(becy[1]/becy[2])*100,pch=16, ylim=c(0,max(Cas9I/(becy[1]/becy[2])*100)),ylab='Cas9 activity (%)', xlab = 'time (h)',cex=1.5)
lines(seq(0,60,0.01), (b/Dp+(-b/Dp)*exp(-Dp*seq(0,60,0.01)))/(becy[1]/becy[2])*100,lwd=3)
legend('bottomright',c('n = 3', paste('+shield =',round(log(2)/becy[2],1),'h'),''),bty='n')
Declaration of modeling functions
Individual Series
ZLWB2fit: fitting to total indels, sweeping 2^21 fold, with predictions from each sweeping all stored and returned
ZLWB2fitPoverF: fitting to total indels, specifiying a perticular p/m factor that is log2 transformed, with all predictions stored and returned
ZLWB2fitDefault: fitting to total indels, a complete model for optimal values of kc kp and km, with all predictions stored and returned
ZLWB2fit_Mutants: fitting to individual indels, sweeping 2^21 fold, with predictions from each sweeping all stored and returned
ZLWB2fitPoverF_Mutants: fitting to individual indels, specifiying a perticular p/m factor that is log2 transformed, with all predictions stored and returned
ZLWB2fit_MutantsDefault: fitting to individual indels, a complete model for optimal values of kc kp and km, kplus1 and kminus7, with all predictions stored and returned
Piled-up for upperbound determination
ZLWB2PiledupOM: fitting to individual indels pipled up with all specified data serieses, a complete model for optimal values of kc km and kp, kplus1 and kminus7, with modelling results returned
ZLWB2PiledupSweep: fitting to individual indels pipled up with all specified data serieses, specifiying a perticular p/m factor, with modelling results returned
ZLWB2PiledupOMMerge: fitting to total indels pipled up with all specified data serieses, a complete model for optimal values of kc kp and km, with modelling results returned, including peakBro
ZLWB2PiledupSweepMerge: fitting to total indels pipled up with all specified data serieses, specifiying a perticular p/m factor, with modelling results returned, including peakBro
UntransfectedFitting: fitting sigmod curve to obtain the untransfected fraction, returning sigmoid factors
##################################################################################
# ZLWB2fit: fitting to total indels, sweeping 2^21 fold, with predictions from each sweeping all stored and returned
##################################################################################
ZLWB2fit = function(time,wt,indel,PoverF=1, U=NA){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
# fitting untransfected
x=time
y=wt/100
z=indel/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U<-ifelse(is.na(U), 1-coef(fitmodel)[1], U)
untransf=U
listkinetc=rep(list(NULL),21);
for (i in 1:21){
perfect=2^11/2^i*PoverF;
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]*perfect
km=parms[2]
b=becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2]/perfect;
km=log(2)/parms[2];
b=becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
return(c((U+P)/(U+P+M)-y))
}
# function for repair results
rxnrate_repair = function(parms,U){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2]/perfect;
km=log(2)/parms[2];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
return(list(s[,2],(P+U)/(P+U+M),M/(P+U+M)))
}
#input data points
df= data.frame(time=x, wt=y*100, indel=z*100)
df[,1]=df[,1]-df[1,1]
df[,2:3]=df[,2:3]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8)
FL = try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))), silent = TRUE)
if(length(FL)>1){
residuals=resid (FL)
listkinetc[[i]]=list(perfectF = perfect,
hlc = FL$par[1],
hlm = FL$par[2],
intact= rxnrate_repair(FL$par,U)[[2]],
broken= rxnrate_repair(FL$par,U)[[1]],
maxBro = max( rxnrate_repair(FL$par,U)[[1]]),
peakBro = seq(0,max(time),0.1)[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant= rxnrate_repair(FL$par,U)[[3]],
residual_sq=sum(residuals^2),
washintact=rxnrate_repairWash(FL$par,U)[[2]],
washmutant=rxnrate_repairWash(FL$par,U)[[3]]
);
}
}
listkinetc = listkinetc[!is.null(listkinetc)];
return(listkinetc);
}
##################################################################################
##################################################################################
# ZLWB2fitPoverF: fitting to total indels, specifiying a perticular p/m factor that is log2 transformed, with all predictions stored and returned
##################################################################################
ZLWB2fitPoverF = function(time,wt,indel,PoverF, U=NA){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
#####
# fitting untransfected
x=time
y=wt/100
z=indel/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U<-ifelse(is.na(U), 1-coef(fitmodel)[1], U)
untransf=U
listkinetc=list(NULL);
perfect=2^PoverF;
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]*perfect
km=parms[2]
b=becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2]/perfect;
km=log(2)/parms[2];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
return(c((U+P)/(U+P+M)-y))
}
# function for repair results
rxnrate_repair = function(parms,U){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2]/perfect;
km=log(2)/parms[2];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
return(list(s[,2],(P+U)/(P+U+M),M/(P+U+M)))
}
# fitting
df= data.frame(time=x, wt=y*100, indel=z*100)
df[,1]=df[,1]-df[1,1]
df[,2:3]=df[,2:3]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8)
FL= try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
if (length(FL)>1){
residuals=resid (FL)
listkinetc=list(perfectF = perfect,
hlc = FL$par[1],
hlm = FL$par[2],
intact=rxnrate_repair(FL$par,U)[[2]],
broken=rxnrate_repair(FL$par,U)[[1]],
maxBro = max(rxnrate_repair(FL$par,U)[[1]]),
peakBro =seq(0,max(time),0.1)[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant=rxnrate_repair(FL$par,U)[[3]],
residual_sq=sum(residuals^2));
}
return(listkinetc);
}
##################################################################################
##################################################################################
# ZLWB2fitDefault: fitting to total indels, a complete model for optimal values of kc kp and km, with all predictions stored and returned
##################################################################################
ZLWB2fitDefault = function(time,wt,indel, U=NA){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
# fitting untransfected
x=time
y=wt/100
z=indel/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U<-ifelse(is.na(U), 1-coef(fitmodel)[1], U)
untransf=U
listkinetc=list(NULL);
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]
km=parms[3]
b=becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
return(c((U+P)/(U+P+M)-y))
}
# function for repair results
rxnrate_repair = function(parms,U){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
return(list(s[,2],(P+U)/(P+U+M),M/(P+U+M)))
}
# fitting
df= data.frame(time=x, wt=y*100, indel=z*100)
df[,1]=df[,1]-df[1,1]
df[,2:3]=df[,2:3]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8,8)
FL= try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
if (length(FL)>1){
residuals=resid (FL)
listkinetc=list(perfectF = FL$par[3]/FL$par[2],
hlc = FL$par[1],
hlm = FL$par[3],
intact=rxnrate_repair(FL$par,U)[[2]],
broken=rxnrate_repair(FL$par,U)[[1]],
maxBro = max(rxnrate_repair(FL$par,U)[[1]]),
peakBro =seq(0,max(time),0.1)[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant=rxnrate_repair(FL$par,U)[[3]],
residual_sq=sum(residuals^2));
}
return(listkinetc);
}
##################################################################################
##################################################################################
# ZLWB2fit_Mutants: fitting to individual indels, sweeping 2^21 fold, with predictions from each sweeping all stored and returned
##################################################################################
ZLWB2fit_Mutants = function(time,wt,indel,PoverF=1,plus1,minus7, U=NA){
# fitting untransfected
x=time
y=wt/100
z=indel/100
is1 = plus1/100;
mn7 = minus7/100;
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
is1 = is1[index];
mn7= mn7[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U<-ifelse(is.na(U), 1-coef(fitmodel)[1], U)
untransf=U
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
listkinetc=rep(list(NULL),21);
for (i in 1:21){
perfect=2^11/2^i*PoverF;
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]*perfect
km=parms[2]
kplus1 = parms[3]
kminus7 = parms[4]
b=becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
r[4]=kplus1*c["B"]#dcC/dt
r[5]=kminus7*c["B"]
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2]/perfect;
km=log(2)/parms[2];
kplus1 = log(2)/parms[3]
kminus7 = log(2)/parms[4]
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0, PL1=0, MN7=0),times=t,func=WB1rxnrate,parms=c(kc,km,kplus1,kminus7))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
return(c(M/(U+P+M)-df[,3],PL1/(U+P+M)-df[,4],MN7/(U+P+M)-df[,5]))
}
# function for repair results
rxnrate_repair = function(parms,U){
kc=log(2)/parms[1];
kp=log(2)/parms[2]/perfect;
km=log(2)/parms[2];
kplus1 = log(2)/parms[3]
kminus7 = log(2)/parms[4]
out=ode(y=c(P=as.numeric(1-U),B=0,M=0, PL1=0, MN7=0),times=t,func=WB1rxnrate,parms=c(kc,km,kplus1,kminus7))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
return(list(s[,2],(P+U)/(P+U+M),M/(P+U+M),PL1/(U+P+M),MN7/(U+P+M)))
}
# fitting
df= data.frame(time=x, wt=y*100, indel=z*100, plus1= is1*100, minus7=mn7*100)
df[,1]=df[,1]-df[1,1]
df[,2:5]=df[,2:5]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8,8,8)
FL= try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
if (length(FL)>1){
residuals=resid (FL)
listkinetc[[i]]=list(perfectF = perfect,
hlc = FL$par[1],
hlm = FL$par[2],
hlpl1 = FL$par[3],
hlmn7 = FL$par[4],
intact=rxnrate_repair(FL$par,U)[[2]],
broken=rxnrate_repair(FL$par,U)[[1]],
maxBro = max(rxnrate_repair(FL$par,U)[[1]]),
peakBro =seq(0,max(time),0.1)[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant=rxnrate_repair(FL$par,U)[[3]],
plus1=rxnrate_repair(FL$par,U)[[4]],
minus7=rxnrate_repair(FL$par,U)[[5]],
residual_sq=sum(residuals^2));
}
}
listkinetc = listkinetc[!is.null(listkinetc)];
return(listkinetc);
}
##################################################################################
##################################################################################
# ZLWB2fitPoverF_Mutants: fitting to individual indels, specifiying a perticular p/m factor that is log2 transformed, with all predictions stored and returned
##################################################################################
ZLWB2fitPoverF_Mutants = function(time,wt,indel,PoverF=1,plus1,minus7, U=NA){
# fitting untransfected
x=time
y=wt/100
z=indel/100
is1 = plus1/100;
mn7 = minus7/100;
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
is1 = is1[index];
mn7= mn7[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U<-ifelse(is.na(U), 1-coef(fitmodel)[1], U)
untransf=U
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
listkinetc=list(NULL);
perfect=2^PoverF;
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]*perfect
km=parms[2]
kplus1 = parms[3]
kminus7 = parms[4]
b = becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
r[4]=kplus1*c["B"]#dcC/dt
r[5]=kminus7*c["B"]
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2]/perfect;
km=log(2)/parms[2];
kplus1 = log(2)/parms[3]
kminus7 = log(2)/parms[4]
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0, PL1=0, MN7=0),times=t,func=WB1rxnrate,parms=c(kc,km,kplus1,kminus7))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
return(c(M/(U+P+M)-df[,3],PL1/(U+P+M)-df[,4],MN7/(U+P+M)-df[,5]))
}
# function for repair results
rxnrate_repair = function(parms,U){
kc=log(2)/parms[1];
kp=log(2)/parms[2]/perfect;
km=log(2)/parms[2];
kplus1 = log(2)/parms[3]
kminus7 = log(2)/parms[4]
out=ode(y=c(P=as.numeric(1-U),B=0,M=0, PL1=0, MN7=0),times=t,func=WB1rxnrate,parms=c(kc,km,kplus1,kminus7))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
return(list(s[,2],(P+U)/(P+U+M),M/(P+U+M),PL1/(U+P+M),MN7/(U+P+M)))
}
# fitting
df= data.frame(time=x, wt=y*100, indel=z*100, plus1= is1*100, minus7=mn7*100)
df[,1]=df[,1]-df[1,1]
df[,2:5]=df[,2:5]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8,8,8)
FL= try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
if (length(FL)>1){
residuals=resid (FL)
listkinetc=list(perfectF = perfect,
hlc = FL$par[1],
hlm = FL$par[2],
hlpl1 = FL$par[3],
hlmn7 = FL$par[4],
intact=rxnrate_repair(FL$par,U)[[2]],
broken=rxnrate_repair(FL$par,U)[[1]],
maxBro = max(rxnrate_repair(FL$par,U)[[1]]),
peakBro =seq(0,max(time),0.1)[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant=rxnrate_repair(FL$par,U)[[3]],
plus1=rxnrate_repair(FL$par,U)[[4]],
minus7=rxnrate_repair(FL$par,U)[[5]],
residual_sq=sum(residuals^2));
}
return(listkinetc);
}
##################################################################################
##################################################################################
# ZLWB2fit_MutantsDefault: fitting to individual indels, a complete model for optimal values of kc kp and km, kplus1 and kminus7, with all predictions stored and returned
##################################################################################
ZLWB2fit_MutantsDefault = function(time,wt,indel,plus1,minus7, U=NA){
# fitting untransfected
x=time
y=wt/100
z=indel/100
is1 = plus1/100;
mn7 = minus7/100;
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
is1 = is1[index];
mn7= mn7[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U<-ifelse(is.na(U), 1-coef(fitmodel)[1], U)
untransf=U
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
listkinetc=list(NULL);
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[5]
km=parms[2]
kplus1 = parms[3]
kminus7 = parms[4]
b=becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
r[4]=kplus1*c["B"]#dcC/dt
r[5]=kminus7*c["B"]
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[5];
km=log(2)/parms[2];
kplus1 = log(2)/parms[3]
kminus7 = log(2)/parms[4]
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0, PL1=0, MN7=0),times=t,func=WB1rxnrate,parms=c(kc,km,kplus1,kminus7,kp))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
return(c(M/(U+P+M)-df[,3],PL1/(U+P+M)-df[,4],MN7/(U+P+M)-df[,5]))
}
# function for repair results
rxnrate_repair = function(parms,U){
kc=log(2)/parms[1];
kp=log(2)/parms[5];
km=log(2)/parms[2];
kplus1 = log(2)/parms[3]
kminus7 = log(2)/parms[4]
out=ode(y=c(P=as.numeric(1-U),B=0,M=0, PL1=0, MN7=0),times=t,func=WB1rxnrate,parms=c(kc,km,kplus1,kminus7,kp))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
return(list(s[,2],(P+U)/(P+U+M),M/(P+U+M),PL1/(U+P+M),MN7/(U+P+M)))
}
# fitting
df= data.frame(time=x, wt=y*100, indel=z*100, plus1= is1*100, minus7=mn7*100)
df[,1]=df[,1]-df[1,1]
df[,2:5]=df[,2:5]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8,8,8,8)
FL= try(modFit(f= WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
if (length(FL)>1){
residuals=resid (FL)
listkinetc=list( perfectF = FL$par[2]/FL$par[5],
hlc = FL$par[1],
hlm = FL$par[2],
hlpl1 = FL$par[3],
hlmn7 = FL$par[4],
intact=rxnrate_repair(FL$par,U)[[2]],
broken=rxnrate_repair(FL$par,U)[[1]],
maxBro = max(rxnrate_repair(FL$par,U)[[1]]),
peakBro =seq(0,max(time),0.1)[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant=rxnrate_repair(FL$par,U)[[3]],
plus1=rxnrate_repair(FL$par,U)[[4]],
minus7=rxnrate_repair(FL$par,U)[[5]],
residual_sq=sum(residuals^2));
}
return(listkinetc);
}
##################################################################################
##################################################################################
# ZLWB2PiledupOM: fitting to individual indels pipled up with all specified data serieses, a complete model for optimal values of kc km and kp, kplus1 and kminus7, with modelling results returned
##################################################################################
ZLWB2PiledupOM = function(time,wt,indel,plus1,minus7, U=NA){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
t= seq(0,max(time*10))/10;
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[5]
km=parms[2]
kplus1 = parms[3]
kminus7 = parms[4]
b=becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
r[4]=kplus1*c["B"]#dcC/dt
r[5]=kminus7*c["B"]
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
res = rep(list(NULL),ncol(wt));
for (i in 1:length(res)){
# fitting untransfected
x=time
y=wt[,i]/100
z=indel[,i]/100
is1 = plus1[,i]/100;
mn7 = minus7[,i]/100;
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
is1 = is1[index];
mn7= mn7[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U<-ifelse(is.na(U), 1-coef(fitmodel)[1], U)
untransf=U;
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[5]
km=log(2)/parms[2];
kplus1 = log(2)/parms[3]
kminus7 = log(2)/parms[4]
out=ode(y=c(P=as.numeric(1-U),B=0,M=0, PL1=0, MN7=0),times=t,func= WB1rxnrate
,parms=c(kc,km,kplus1,kminus7,kp))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
res[[i]] =c(M/(U+P+M)-z,PL1/(U+P+M)-is1,MN7/(U+P+M)-mn7)
}
return(unlist(res))
}
parms=c(8,8,8,8,8)
FL= try(modFit(f= WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
return(FL);
}
##################################################################################
##################################################################################
# ZLWB2PiledupSweep: fitting to individual indels pipled up with all specified data serieses, specifiying a perticular p/m factor, with modelling results returned
##################################################################################
ZLWB2PiledupSweep = function(time,wt,indel,plus1,minus7, U=NA){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
t= seq(0,max(time*10))/10;
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]*sweep
km=parms[2]
kplus1 = parms[3]
kminus7 = parms[4]
b=becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
r[4]=kplus1*c["B"]#dcC/dt
r[5]=kminus7*c["B"]
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
res = rep(list(NULL),ncol(wt));
for (i in 1:length(res)){
# fitting untransfected
x=time
y=wt[,i]/100
z=indel[,i]/100
is1 = plus1[,i]/100;
mn7 = minus7[,i]/100;
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
is1 = is1[index];
mn7= mn7[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U<-ifelse(is.na(U), 1-coef(fitmodel)[1], U)
untransf=U;
tmax = max(time);
kc=log(2)/(parms[1])
kp=log(2)/(parms[2]*sweep)
km=log(2)/(parms[2])
kplus1 = log(2)/(parms[3])
kminus7 = log(2)/(parms[4])
out=ode(y=c(P=as.numeric(1-U),B=0,M=0, PL1=0, MN7=0),times=t,func= WB1rxnrate
,parms=c(kc,km,kplus1,kminus7,kp))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
res[[i]] =c(M/(U+P+M)-z,PL1/(U+P+M)-is1,MN7/(U+P+M)-mn7)
}
return(unlist(res))
}
parms=c(8,8,8,8)
FL= try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE);
return(list(perfectF = sweep,
hlc = round(FL$par[1],1),
hlm = round(FL$par[2],1),
hlpl1 = round(FL$par[3],1),
hlmn7 = round(FL$par[4],1),
residual_sq=sum(resid(FL)^2)));
}
#######################################################################
# ZLWB2PiledupOMMerge: fitting to total mutations pipled up with all specified data serieses, a complete model for optimal values of kc kp and km, with modelling results returned, including peakBro
#######################################################################
ZLWB2PiledupOMMerge = function(time,wt,indel){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513, 1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
#####
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]
km=parms[3]
b = becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
res = rep(list(NULL),ncol(wt));
for (i in 1:length(res)){
# fitting untransfected
x=time
y=wt[,i]/100
z=indel[,i]/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U = 1-coef(fitmodel)[1]
untransf=U;
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2]
km=log(2)/parms[3];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func= WB1rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
res[[i]] =c(M/(U+P+M)-z)
}
return(unlist(res))
}
parms=c(8,8,8)
FL= try(modFit(f= WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
out=ode(y=c(P=1,B=0,M=0),times=t,func= WB1rxnrate,parms=log(2)/FL$par)
return(list(FL,peakBro=out[which.max(out[,3]),1]));
}
########################################################################
#ZLWB2PiledupSweepMerge: fitting to total mutations pipled up with all specified data serieses, specifiying a perticular p/m factor, with modelling results returned,including peakBro
#######################################################################
ZLWB2PiledupSweepMerge = function(time,wt,indel){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513, 1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
#####
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]*sweep
km=parms[2]
b = becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
res = rep(list(NULL),ncol(wt));
for (i in 1:length(res)){
# fitting untransfected
x=time
y=wt[,i]/100
z=indel[,i]/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U = 1-coef(fitmodel)[1]
untransf=U;
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/(parms[2]*sweep)
km=log(2)/parms[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func= WB1rxnrate,parms=c(kc,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
res[[i]] =c(M/(U+P+M)-z)
}
return(unlist(res))
}
t= seq(0,max(time)*10)/10
parms=c(8,8)
FL= try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE);
out=ode(y=c(P=1,B=0,M=0),times=t,func= WB1rxnrate,parms= c(log(2)/FL$par));
return(list(perfectF = sweep,
hlc = FL$par[1],
hlm = FL$par[2],
residual_sq=sum(resid(FL)^2),
peakBro=out[which.max(out[,3]),1],
maxBro=max(out[,3]),
stdErr = summary(FL)[[11]][,2])
);
}
##################################################################################
#ZLWB2hlcPiledupSweepMerge
##################################################################################
ZLWB2hlcPiledupSweepMerge = function(time,wt,indel){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513, 1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
#####
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=log(2)/sweep
kp=parms[1]
km=parms[2]
b = becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
res = rep(list(NULL),ncol(wt));
for (i in 1:length(res)){
# fitting untransfected
x=time
y=wt[,i]/100
z=indel[,i]/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U = 1-coef(fitmodel)[1]
untransf=U;
tmax = max(time);
kc=log(2)/sweep;
kp=log(2)/parms[1]
km=log(2)/parms[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func= WB1rxnrate,parms=c(kp,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
res[[i]] =c(M/(U+P+M)-z)
}
return(unlist(res))
}
t= seq(0,max(time)*10)/10
parms=c(8,8)
FL= try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE);
out=ode(y=c(P=1,B=0,M=0),times=t,func= WB1rxnrate,parms= c(log(2)/FL$par));
return(list(hlc = log(2)/sweep,
hlp= round(FL$par[1],1),
hlm = round(FL$par[2],1),
residual_sq=sum(resid(FL)^2),
peakBro=out[which.max(out[,3]),1],
maxBro = max(out[,3]))
);
}
##################################################################################
# function all-time-point fitting
##################################################################################
ZLWB2fitDefaultAllTP = function(DF, U=NA){
time = DF$time;
wt = DF$wt;
indel = DF$mut;
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
# fitting untransfected
x=time
y=wt/100
z=indel/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
untransf=U
listkinetc=list(NULL);
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1]
kp=parms[2]
km=parms[3]
b=becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
P = P[DF[,4]];
M = M[DF[,4]];
return(c((U+P)/(U+P+M)-df[,2]))
}
# function for repair results
rxnrate_repair = function(parms,U){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
P = s[,1];
M= s[,3];
return(list(s[,2],(P+U)/(P+U+M),M/(P+U+M)))
}
# fitting
df= data.frame(time=x, wt=y*100, indel=z*100)
df[,2:3]=df[,2:3]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8,8)
FL= try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
if (length(FL)>1){
residuals=resid (FL)
listkinetc=list(perfectF = FL$par[3]/FL$par[2],
time = DF[,1],
hlc = FL$par[1],
hlm = FL$par[3],
intact=rxnrate_repair(FL$par,U)[[2]],
broken=rxnrate_repair(FL$par,U)[[1]],
maxBro = max(rxnrate_repair(FL$par,U)[[1]]),
peakBro =seq(0,max(time),0.1)[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant=rxnrate_repair(FL$par,U)[[3]],
residual_sq=sum(residuals^2),
Untf=U);
}
return(listkinetc);
}
##################################################################################
# UntransfectedFitting: fitting sigmod curve to obtain the untransfected fraction, returning sigmoid factors
##################################################################################
UntransfectedFitting = function(time,wt,indel){
# fitting untransfected
x=time
y=wt/100
z=indel/100
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
return(coef(fitmodel))
}
##################################################################################Import data of timeseries
# loading data
wildtype_tot_LBR2 <- read.delim("wildtype_tot_LBR2.txt", stringsAsFactors=FALSE);
mutations_tot_LBR2 <- read.delim("mutations_tot_LBR2.txt", stringsAsFactors=FALSE)
wildtype_tot_LBR2 = wildtype_tot_LBR2[-nrow(wildtype_tot_LBR2),];
mutations_tot_LBR2 = mutations_tot_LBR2[-nrow(mutations_tot_LBR2),];
wildtype_tot_LBR8 <- read.delim("wildtype_tot_LBR8.txt", stringsAsFactors=FALSE);
mutations_tot_LBR8 <- read.delim("mutations_tot_LBR8.txt", stringsAsFactors=FALSE)
wildtype_tot_LBR8 = wildtype_tot_LBR8[-nrow(wildtype_tot_LBR8),];
mutations_tot_LBR8 = mutations_tot_LBR8[-nrow(mutations_tot_LBR8),];
wildtype_tot_AAVS1 <- read.delim("wildtype_tot_AAVS1.txt", stringsAsFactors=FALSE);
mutations_tot_AAVS1 <- read.delim("mutations_tot_AAVS1.txt", stringsAsFactors=FALSE)
wildtype_tot_AAVS1 = wildtype_tot_AAVS1[-nrow(wildtype_tot_AAVS1),];
mutations_tot_AAVS1 = mutations_tot_AAVS1[-nrow(mutations_tot_AAVS1),];
wildtype_tot_chr11 <- read.delim("wildtype_tot_chr11.txt", stringsAsFactors=FALSE);
mutations_tot_chr11 <- read.delim("mutations_tot_chr11.txt", stringsAsFactors=FALSE)
wildtype_tot_chr11 = wildtype_tot_chr11[-nrow(wildtype_tot_chr11),];
mutations_tot_chr11 = mutations_tot_chr11[-nrow(mutations_tot_chr11),];
#without inhibitor
wt_1=wildtype_tot_LBR2[,-c(4,6,8,10,12)]
indel_1=mutations_tot_LBR2[,-c(4,6,8,10,12)]
wt_LBR8=wildtype_tot_LBR8
indel_LBR8=mutations_tot_LBR8
wt_AAVS1=wildtype_tot_AAVS1
indel_AAVS1=mutations_tot_AAVS1
wt_chr11=wildtype_tot_chr11
indel_chr11=mutations_tot_chr11
#with inhibitor
wt_2=wildtype_tot_LBR2[,c(4,6,8,10)]
indel_2=mutations_tot_LBR2[,c(4,6,8,10)]
# extract time point information
time = as.numeric(row.names(wildtype_tot_LBR2));Modeling individual series for total indels
Apply function ZLWB2fitDefault to individual series, spliting into without and with NU7441 inhibitor
# fitting for noinhibitor
U =NA
R_sq=NA
kcmax=kp=km=NA
noinhibitor_total_individual_series = rep(list(NULL),ncol(wt_1));
for (i in 1:ncol(wt_1)){
noinhibitor_total_individual_series[[i]]=ZLWB2fitDefault(time,wt_1[,i],indel_1[,i])#, U=FACS_m);
AA = UntransfectedFitting(time,wt_1[,i],indel_1[,i]);
U[i]= 1-AA[1]
# data
plot(time,wt_1[,i],main=paste(colnames(wt_1)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
# untransfected
plot(time,wt_1[,i],main=paste('w/o NU7441:',colnames(wt_1)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
lines(seq(0,60,0.01),(1-AA[1]/(1 + exp(-AA[2] * (seq(0,60,0.01)-AA[3]))))*100,lwd=3);
legend('topright',paste('U =',round(100-AA[1]*100),'%'),bty='n')
# fitting intact
plot(time,wt_1[,i],main=paste('w/o NU7441:',colnames(wt_1)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)',col=2);
lines(time[which(is.finite(wt_1[,i]))],noinhibitor_total_individual_series[[i]]$intact*100,lwd=3,col=2)
R_sq[i]=cor(noinhibitor_total_individual_series[[i]]$intact,wt_1[which(is.finite(wt_1[,i])),i])^2;
kcmax[i]= log(2)/noinhibitor_total_individual_series[[i]]$hlc;
kp[i]= log(2)/(noinhibitor_total_individual_series[[i]]$hlm/noinhibitor_total_individual_series[[i]]$perfectF);
km[i]=log(2)/noinhibitor_total_individual_series[[i]]$hlm;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting mutants
plot(time,indel_1[,i],main=paste('w/o NU7441:',colnames(wt_1)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='mutated/(intact+mutated) (%)',xlab='time (h)',col=3);
lines(time[which(is.finite(wt_1[,i]))],noinhibitor_total_individual_series[[i]]$mutant*100,lwd=3,col=3)
R_sq[i]=cor(noinhibitor_total_individual_series[[i]]$intact,wt_1[which(is.finite(wt_1[,i])),i])^2;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting broken
plot(seq(0,max(time),0.1),main=paste('w/o NU7441:',colnames(wt_1)[i]),noinhibitor_total_individual_series[[i]]$broken*100,lwd=3,col=4,type='l',ylab='broken (%)',xlab='time (h)')
abline(v=noinhibitor_total_individual_series[[i]]$peakBro,col=4,lwd=3,lty=2)
abline(h=0,col=4,lwd=3,lty=2)
legend('topright',legend=bquote(tau == .(noinhibitor_total_individual_series[[i]]$peakBro)~' h '), bty='n')
#plot rates
bplt=barplot(main=paste('w/o NU7441:',colnames(wt_1)[i]),c(kcmax[i],kp[i],km[i]),col=c(4,1,2),names.arg=c(expression('k'['c']*'max'), expression('k'['p']), expression('k'['m'])),horiz=TRUE,las=2,xlab=expression('h'^-1));
rg=range(c(kcmax[i],kp[i],km[i]));
text(mean(rg),bplt[1,1],bquote(.(round(kcmax[i]/10^floor(log10(kcmax[i])),1)) *'x 10' ^ .(floor(log10(kcmax[i])))),col=3,cex=2)
text(mean(rg),bplt[2,1],bquote(.(round(kp[i]/10^floor(log10(kp[i])),1)) *'x 10' ^ .(floor(log10(kp[i])))),col=3,cex=2)
text(mean(rg),bplt[3,1],bquote(.(round(km[i]/10^floor(log10(km[i])),1)) *'x 10' ^ .(floor(log10(km[i])))),col=3,cex=2)
}
print(R_sq)## [1] 0.9977445 0.9964705 0.9979381 0.9978886 0.9944548 0.9967605 0.9852044print(mean(R_sq))## [1] 0.9952088print(sd(R_sq))## [1] 0.004577412#################################################################
# fitting for with inhibitor
#################################################################
R_sq_i=NA
kcmax_i=kp_i=km_i=NA
withinhibitor_total_individual_series = rep(list(NULL),ncol(wt_2));
for (i in 1:ncol(wt_2)){
withinhibitor_total_individual_series[[i]]=ZLWB2fitDefault(time,wt_2[,i],indel_2[,i]);
AA = UntransfectedFitting(time,wt_2[,i],indel_2[,i]);
# data
plot(time,wt_2[,i],main=paste(colnames(wt_2)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
# untransfected
plot(time,wt_2[,i],main=paste('with NU7441:',colnames(wt_2)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
lines(seq(0,60,0.01),(1-AA[1]/(1 + exp(-AA[2] * (seq(0,60,0.01)-AA[3]))))*100,lwd=3);
legend('topright',paste('U =',round(100-AA[1]*100),'%'),bty='n')
# fitting intact
plot(time,wt_2[,i],main=paste('with NU7441:',colnames(wt_2)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)',col=2);
lines(time[which(is.finite(wt_2[,i]))],withinhibitor_total_individual_series[[i]]$intact*100,lwd=3,col=2)
R_sq_i[i]=cor(withinhibitor_total_individual_series[[i]]$intact,wt_2[which(is.finite(wt_2[,i])),i])^2;
kcmax_i= log(2)/withinhibitor_total_individual_series[[i]]$hlc;
kp_i= log(2)/(withinhibitor_total_individual_series[[i]]$hlm/withinhibitor_total_individual_series[[i]]$perfectF);
km_i=log(2)/withinhibitor_total_individual_series[[i]]$hlm;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting mutants
plot(time,indel_2[,i],main=paste('with NU7441:',colnames(wt_2)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='mutated/(intact+mutated) (%)',xlab='time (h)',col=3);
lines(time[which(is.finite(wt_2[,i]))],withinhibitor_total_individual_series[[i]]$mutant*100,lwd=3,col=3)
R_sq_i[i]=cor(withinhibitor_total_individual_series[[i]]$intact,wt_2[which(is.finite(wt_2[,i])),i])^2;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting broken
plot(seq(0,max(time),0.1),main=paste('with NU7441:',colnames(wt_2)[i]),withinhibitor_total_individual_series[[i]]$broken*100,lwd=3,col=4,type='l',ylab='broken (%)',xlab='time (h)')
abline(v=withinhibitor_total_individual_series[[i]]$peakBro,col=4,lwd=3,lty=2)
abline(h=0,col=4,lwd=3,lty=2)
legend('topright',legend=bquote(tau == .(withinhibitor_total_individual_series[[i]]$peakBro)~' h '), bty='n')
#plot rates
bplt=barplot(main=paste('with NU7441:',colnames(wt_1)[i]),c(kcmax_i,kp_i,km_i),col=c(4,1,2),names.arg=c(expression('k'['c']*'max'), expression('k'['p']), expression('k'['m'])),horiz=TRUE,las=2,xlab=expression('h'^-1));
rg=range(c(kcmax_i,kp_i,km_i));
text(mean(rg),bplt[1,1],bquote(.(round(kcmax_i/10^floor(log10(kcmax_i)),1)) *'x 10' ^ .(floor(log10(kcmax_i)))),col=3,cex=2)
text(mean(rg),bplt[2,1],bquote(.(round(kp_i/10^floor(log10(kp_i)),1)) *'x 10' ^ .(floor(log10(kp_i)))),col=3,cex=2)
text(mean(rg),bplt[3,1],bquote(.(round(km_i/10^floor(log10(km_i)),1)) *'x 10' ^ .(floor(log10(km_i)))),col=3,cex=2)
}
print(R_sq_i)## [1] 0.9957952 0.9936801 0.9954931 0.9920376print(mean(R_sq_i))## [1] 0.9942515print(sd(R_sq_i))## [1] 0.001746663Collect individual data series and represent parameters
split into without and with NU7441
library(beeswarm);
# hlc
beeswarm(list(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlc})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['c']*'max'*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(noinhibitor_total_individual_series,function(x){x$hlc})),1),'±',round(sd(sapply(noinhibitor_total_individual_series,function(x){x$hlc})),1)),paste(round(mean(sapply(withinhibitor_total_individual_series,function(x){x$hlc})),1),'+/-',round(sd(sapply(withinhibitor_total_individual_series,function(x){x$hlc})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlc});
d2 = log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlc});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
# hlp
beeswarm(list(log(2)/(sapply(noinhibitor_total_individual_series,function(x){x$hlm})/sapply(noinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(withinhibitor_total_individual_series,function(x){x$hlm})/sapply(withinhibitor_total_individual_series,function(x){x$perfectF}))),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['p']*' (h'^-1*')'));
lgd = legend('bottomright',c(paste(round(mean(log(2)/(sapply(noinhibitor_total_individual_series,function(x){x$hlm})/sapply(noinhibitor_total_individual_series,function(x){x$perfectF}))),2),'+/-',round(sd(log(2)/(sapply(noinhibitor_total_individual_series,function(x){x$hlm})/sapply(noinhibitor_total_individual_series,function(x){x$perfectF}))),2)),paste(round(mean(log(2)/(sapply(withinhibitor_total_individual_series,function(x){x$hlm})/sapply(withinhibitor_total_individual_series,function(x){x$perfectF}))),2),'+/-',round(sd(log(2)/(sapply(withinhibitor_total_individual_series,function(x){x$hlm})/sapply(withinhibitor_total_individual_series,function(x){x$perfectF}))),2))),text.col=c(0,0),bty='n')
d1 = (log(2)/(sapply(noinhibitor_total_individual_series,function(x){x$hlm})/sapply(noinhibitor_total_individual_series,function(x){x$perfectF})));
d2 = (log(2)/(sapply(withinhibitor_total_individual_series,function(x){x$hlm})/sapply(withinhibitor_total_individual_series,function(x){x$perfectF})));
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
# hlm
beeswarm(list(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlm})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['m']*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(noinhibitor_total_individual_series,function(x){x$hlm})),1),'±',round(sd(sapply(noinhibitor_total_individual_series,function(x){x$hlm})),1)),paste(round(mean(sapply(withinhibitor_total_individual_series,function(x){x$hlm})),1),'+/-',round(sd(sapply(withinhibitor_total_individual_series,function(x){x$hlm})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlm});
d2 = log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlm});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
# peakBro
beeswarm(list(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}),sapply(withinhibitor_total_individual_series,function(x){x$peakBro})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=bquote(tau * ' (h)' ))
legend('bottomright',c(paste(round(mean(sapply(noinhibitor_total_individual_series,function(x){x$peakBro})),1),'+/-',round(sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro})),1)),paste(round(mean(sapply(withinhibitor_total_individual_series,function(x){x$peakBro})),1),'+/-',round(sd(sapply(withinhibitor_total_individual_series,function(x){x$peakBro})),1))),text.col=c(4,2),bty='n')
Compare the ZLWB2 model with the simple model
for comparison with ZLWB2 by F-statistic
1: invididual series
2: piled-up
# extract time point information
time = as.numeric(row.names(wildtype_tot_LBR2));
#### no inhibitor
###individual series
noinhibitor_simplemodel_series = rep(list(NULL),ncol(wt_1));
for (i in 1:ncol(wt_1)){
noinhibitor_simplemodel_series[[i]]=simpleDefault(time,wt_1[,i],indel_1[,i]);
}
df2 = min(apply(wt_1,2,function(x){sum(as.numeric(!is.na(x)))-3}));
rg = range(c(log2(sapply(noinhibitor_simplemodel_series,function(x){x$residual_sq})),log2(sapply(noinhibitor_total_individual_series,function(x){x$residual_sq}))));
plot(main='w/o NU7441: individual series',log2(sapply(noinhibitor_simplemodel_series,function(x){x$residual_sq})),log2(sapply(noinhibitor_total_individual_series,function(x){x$residual_sq})),xlim=rg,ylim=rg,pch=16,col=4,cex=2,xlab=bquote('log'[2]*'(sum of resid'^2*'): linear ODE'),ylab=bquote('log'[2]*'(sum of resid'^2*'): non-linear ODE'));
abline(-log2(qf(0.95,df2,df2)),1,lwd=2,lty=2,col=3)
abline(-log2(qf(0.5,df2,df2)),1,lwd=2,lty=2)
abline(log2(qf(0.95,df2,df2)),1,lwd=2,lty=2,col=6)
text(mean(rg)+log2(qf(0.95,df2,df2))/2,mean(rg)-log2(qf(0.95,df2,df2))/2,'p=0.05',pos=4,col=3)
text(mean(rg)-log2(qf(0.95,df2,df2))/2,mean(rg)+log2(qf(0.95,df2,df2))/2,'p=0.05',pos=2,col=6)
text(mean(rg),log2(qf(0.5,df2,df2))+mean(rg),'p=0.5')
legend('topleft','linear ODE is better',bty='n',text.col=6)
legend('bottomright','non-linear ODE is better',bty='n',text.col=3)
### optimal fitting for piled up
ZLWB2optimalParaR = ZLWB2PiledupOMMerge(time,wt_1,indel_1)[[1]]$residuals;
simpleoptimalParaR = simplePiledupOMMerge(time,wt_1,indel_1)$residuals;
rg=range(rg,log2(sum(simpleoptimalParaR^2)),log2(sum(ZLWB2optimalParaR^2)));
plot(main='w/o NU7441: piled-up',log2(sapply(noinhibitor_simplemodel_series,function(x){x$residual_sq})),log2(sapply(noinhibitor_total_individual_series,function(x){x$residual_sq})),xlim=rg,ylim=rg,pch=16,col=4,cex=2,xlab=bquote('log'[2]*'(sum of resid'^2*'): linear ODE'),ylab=bquote('log'[2]*'(sum of resid'^2*'): non-linear ODE'),type='n');
df2 = sum(!is.na(wt_1))-3;
abline(-log2(qf(0.95,df2,df2)),1,lwd=2,lty=2,col=3)
abline(-log2(qf(0.5,df2,df2)),1,lwd=2,lty=2)
abline(log2(qf(0.95,df2,df2)),1,lwd=2,lty=2,col=6)
points(log2(sum(simpleoptimalParaR^2)),log2(sum(ZLWB2optimalParaR^2)),pch='*',cex=3,col=4)
text(mean(rg)+log2(qf(0.95,df2,df2))/2,mean(rg)-log2(qf(0.95,df2,df2))/2,'p=0.05',pos=4,col=4)
text(mean(rg)+log2(qf(0.95,df2,df2))/2,mean(rg)-log2(qf(0.95,df2,df2))/2,'p=0.05',pos=4,col=3)
text(mean(rg)-log2(qf(0.95,df2,df2))/2,mean(rg)+log2(qf(0.95,df2,df2))/2,'p=0.05',pos=2,col=6)
text(mean(rg),log2(qf(0.5,df2,df2))+mean(rg),'p=0.5')
legend('topleft','linear ODE is better',bty='n',text.col=6)
legend('bottomright','non-linear ODE is better',bty='n',text.col=3)
#### with inhibitor
###individual series
withinhibitor_simplemodel_sereis = rep(list(NULL),ncol(wt_2));
for (i in 1:ncol(wt_2)){
withinhibitor_simplemodel_sereis[[i]]=simpleDefault(time,wt_2[,i],indel_2[,i]);
}
df2 = min(apply(wt_2,2,function(x){sum(as.numeric(!is.na(x)))-3}));
rg = range(c(log2(sapply(withinhibitor_simplemodel_sereis,function(x){x$residual_sq})),log2(sapply(withinhibitor_total_individual_series,function(x){x$residual_sq}))));
plot(main='with NU7441: individual series',log2(sapply(withinhibitor_simplemodel_sereis,function(x){x$residual_sq})),log2(sapply(withinhibitor_total_individual_series,function(x){x$residual_sq})),xlim=rg,ylim=rg,pch=16,col=2,cex=2,xlab=bquote('log'[2]*'(sum of resid'^2*'): linear ODE'),ylab=bquote('log'[2]*'(sum of resid'^2*'): non-linear ODE'));
abline(-log2(qf(0.95,df2,df2)),1,lwd=2,lty=2,col=3)
abline(-log2(qf(0.5,df2,df2)),1,lwd=2,lty=2)
abline(log2(qf(0.95,df2,df2)),1,lwd=2,lty=2,col=6)
text(mean(rg)+log2(qf(0.95,df2,df2))/2,mean(rg)-log2(qf(0.95,df2,df2))/2,'p=0.05',pos=4,col=3)
text(mean(rg)-log2(qf(0.95,df2,df2))/2,mean(rg)+log2(qf(0.95,df2,df2))/2,'p=0.05',pos=2,col=6)
text(mean(rg),log2(qf(0.5,df2,df2))+mean(rg),'p=0.5')
legend('topleft','linear ODE is better',bty='n',text.col=6)
legend('bottomright','non-linear ODE is better',bty='n',text.col=3)
### optimal fitting for piled up
ZLWB2optimalParaR = ZLWB2PiledupOMMerge(time,wt_2,indel_2)[[1]]$residuals;
simpleoptimalParaR = simplePiledupOMMerge(time,wt_2,indel_2)$residuals;
rg=range(rg,log2(sum(simpleoptimalParaR^2)),log2(sum(ZLWB2optimalParaR^2)));
plot(main='with NU7441: piled-up',log2(sapply(withinhibitor_simplemodel_sereis,function(x){x$residual_sq})),log2(sapply(withinhibitor_total_individual_series,function(x){x$residual_sq})),xlim=rg,ylim=rg,pch=16,col=2,cex=2,xlab=bquote('log'[2]*'(sum of resid'^2*'): linear ODE'),ylab=bquote('log'[2]*'(sum of resid'^2*'): non-linear ODE'),type='n');
df2 = sum(!is.na(wt_2))-3;
abline(-log2(qf(0.95,df2,df2)),1,lwd=2,lty=2,col=3)
abline(-log2(qf(0.5,df2,df2)),1,lwd=2,lty=2)
abline(log2(qf(0.95,df2,df2)),1,lwd=2,lty=2,col=6)
points(log2(sum(simpleoptimalParaR^2)),log2(sum(ZLWB2optimalParaR^2)),pch='*',cex=3,col=2)
text(mean(rg)+log2(qf(0.95,df2,df2))/2,mean(rg)-log2(qf(0.95,df2,df2))/2,'p=0.05',pos=4,col=3)
text(mean(rg)-log2(qf(0.95,df2,df2))/2,mean(rg)+log2(qf(0.95,df2,df2))/2,'p=0.05',pos=2,col=6)
text(mean(rg),log2(qf(0.5,df2,df2))+mean(rg),'p=0.5')
legend('topleft','linear ODE is better',bty='n',text.col=6)
legend('bottomright','non-linear ODE is better',bty='n',text.col=3)
Fit on all data points for 4 loci
time = seq(0,60,0.1);
t = time;
t2 = which(time%in%time_all)
res_LBR2 <- NA
res_LBR8 <- NA
res_AAVS1 <- NA
res_fLAD <- NA
UO<- NA
kc_LBR2 <- kc_LBR8 <-kc_AAVS1 <-kc_chr11 <-NA
kp_LBR2 <- kp_LBR8 <-kp_AAVS1 <-kp_chr11 <-NA
km_LBR2 <- km_LBR8 <-km_AAVS1 <-km_chr11 <-NA
for(i in 0:30){
print(i)
UO[i+1]=(60+i)
#LBR2
XXX = wt_1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(UO[i+1]))/100)
kc_LBR2[i+1] = log(2)/lll$hlc
kp_LBR2[i+1] = log(2)/(lll$hlm/lll$perfectF)
km_LBR2[i+1] = log(2)/lll$hlm
LBR2_av <- rowMeans(wt_1)
R_sq=(cor(lll$intact[t2][which(is.finite(LBR2_av))],LBR2_av[which(is.finite(LBR2_av))])^2);
res_LBR2[i+1] <- lll$residual_sq
print(c(kc_LBR2[i+1], kp_LBR2[i+1], km_LBR2[i+1]))
#plot(DF[,1],DF[,2],ylim=c(0,100),main='LBR2 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
#lines(time_all, rowMeans(wt_1), type="p", pch=20, cex=1.5, col=2)
#lines(time,lll$intact*100,lwd=2,col=2)
#text(x=55, y=90, labels=paste("kc = ", formatC(kc_LBR2[i+1], digits = 2),"\n", "kp =", formatC(kp_LBR2[i+1], digits=2), "\n", "km =", formatC(km_LBR2[i+1],digits=2), "\n", "R^2 =", formatC(R_sq,digits=2)))
#plot(time,lll$broken*100,main='LBR2 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
#LBR8
XXX = wt_LBR8;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(UO[i+1]))/100)
kc_LBR8[i+1] = log(2)/lll$hlc
kp_LBR8[i+1] = log(2)/(lll$hlm/lll$perfectF)
km_LBR8[i+1] = log(2)/lll$hlm
LBR8_av <- rowMeans(wt_LBR8)
R_sq=(cor(lll$intact[t2][which(is.finite(LBR8_av))],LBR8_av[which(is.finite(LBR8_av))])^2);
res_LBR8[i+1] <- lll$residual_sq
print(c(kc_LBR8[i+1], kp_LBR8[i+1], km_LBR8[i+1]))
#plot(DF[,1],DF[,2],ylim=c(0,100),main='LBR8 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
#lines(time_all, rowMeans(wt_LBR8), type="p", pch=20, cex=1.5, col=2)
#lines(time,lll$intact*100,lwd=2,col=2)
#text(x=55, y=90, labels=paste("kc = ", formatC(kc_LBR8[i+1], digits = 2),"\n", "kp =", formatC(kp_LBR8[i+1], digits=2), "\n", "km =", formatC(km_LBR8[i+1],digits=2), "\n", "R^2 =", formatC(R_sq,digits=2)))
#plot(time,lll$broken*100,main='LBR8 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
#AAVS1
XXX = wt_AAVS1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(UO[i+1]))/100)
kc_AAVS1[i+1] = log(2)/lll$hlc
kp_AAVS1[i+1] = log(2)/(lll$hlm/lll$perfectF)
km_AAVS1[i+1] = log(2)/lll$hlm
AAVS1_av <- rowMeans(wt_AAVS1)
R_sq=(cor(lll$intact[t2][which(is.finite(AAVS1_av))],AAVS1_av[which(is.finite(AAVS1_av))])^2);
res_AAVS1[i+1] <- lll$residual_sq
print(c(kc_AAVS1[i+1], kp_AAVS1[i+1], km_AAVS1[i+1]))
#plot(DF[,1],DF[,2],ylim=c(0,100),main='AAVS1 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
#lines(time_all, rowMeans(wt_AAVS1), type="p", pch=20, cex=1.5, col=2)
#lines(time,lll$intact*100,lwd=2,col=2)
#text(x=55, y=90, labels=paste("kc = ", formatC(kc_AAVS1[i+1], digits = 2),"\n", "kp =", formatC(kp_AAVS1[i+1], digits=2), "\n", "km =", formatC(km_AAVS1[i+1],digits=2), "\n", "R^2 =", formatC(R_sq,digits=2)))
#plot(time,lll$broken*100,main='AAVS1 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
#chr11
XXX = wt_chr11;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(UO[i+1]))/100)
chr11_av <- rowMeans(wt_chr11)
R_sq=(cor(lll$intact[t2][which(is.finite(chr11_av))],chr11_av[which(is.finite(chr11_av))])^2);
res_fLAD[i+1] <- lll$residual_sq
kc_chr11[i+1] = log(2)/lll$hlc
kp_chr11[i+1] = log(2)/(lll$hlm/lll$perfectF)
km_chr11[i+1] = log(2)/lll$hlm
print(c(kc_chr11[i+1], kp_chr11[i+1], km_chr11[i+1]))
#plot(DF[,1],DF[,2],ylim=c(0,100),main='chr11 fitting',pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
#lines(time_all, rowMeans(wt_chr11), type="p", pch=20, cex=1.5, col=2)
#lines(time,lll$intact*100,lwd=2,col=2)
#text(x=55, y=90, labels=paste("kc = ", formatC(kc_chr11[i+1], digits = 2),"\n", "kp =", formatC(kp_chr11[i+1], digits=2), "\n", "km =", formatC(km_chr11[i+1],digits=2), "\n", "R^2 =", formatC(R_sq,digits=2)))
#plot(time,lll$broken*100,main='chr11 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
}## [1] 0
## [1] 1.354226e-01 7.288859e-05 1.256846e-01
## [1] 0.07776440 0.05450642 0.03524548
## [1] 9.447550e-02 2.258394e-07 1.500398e-01
## [1] 8.429754e-02 5.738746e-05 7.304851e-02
## [1] 1
## [1] 1.323214e-01 5.503928e-05 1.232747e-01
## [1] 0.08205768 0.05550532 0.03239580
## [1] 8.958473e-02 4.954924e-09 1.580662e-01
## [1] 8.283019e-02 6.172753e-05 7.106581e-02
## [1] 2
## [1] 1.297436e-01 5.142339e-05 1.202187e-01
## [1] 0.08618475 0.05629992 0.02993429
## [1] 8.451145e-02 2.315390e-10 1.700160e-01
## [1] 8.142697e-02 5.383457e-05 6.907993e-02
## [1] 3
## [1] 1.267384e-01 5.575692e-05 1.181341e-01
## [1] 0.09016220 0.05694934 0.02778514
## [1] 7.955762e-02 4.129602e-09 1.859130e-01
## [1] 8.039069e-02 5.681629e-05 6.670909e-02
## [1] 4
## [1] 1.237666e-01 3.417967e-05 1.161802e-01
## [1] 0.09399536 0.05750454 0.02589558
## [1] 7.493728e-02 4.957779e-09 2.055746e-01
## [1] 7.911304e-02 4.795298e-05 6.475409e-02
## [1] 5
## [1] 1.207409e-01 3.090619e-05 1.145827e-01
## [1] 0.09770543 0.05798980 0.02421706
## [1] 7.076231e-02 2.247715e-08 2.286494e-01
## [1] 0.0779040854 0.0000443138 0.0628078599
## [1] 6
## [1] 0.1171627002 0.0000225246 0.1142474696
## [1] 0.10129295 0.05843931 0.02271987
## [1] 6.704096e-02 2.959635e-08 2.549650e-01
## [1] 7.659559e-02 3.198901e-06 6.104514e-02
## [1] 7
## [1] 1.130083e-01 1.027993e-05 1.154003e-01
## [1] 0.10477457 0.05886481 0.02137352
## [1] 6.373068e-02 3.004524e-08 2.846062e-01
## [1] 7.533423e-02 3.631491e-05 5.938497e-02
## [1] 8
## [1] 1.077245e-01 8.662677e-09 1.196603e-01
## [1] 0.10814702 0.05929333 0.02016053
## [1] 6.077510e-02 5.145459e-08 3.179005e-01
## [1] 7.429583e-02 3.838824e-05 5.748890e-02
## [1] 9
## [1] 1.012003e-01 3.537226e-09 1.286496e-01
## [1] 0.11143445 0.05971845 0.01905680
## [1] 5.812024e-02 1.242720e-07 3.554078e-01
## [1] 7.298043e-02 1.974186e-05 5.601286e-02
## [1] 10
## [1] 9.423204e-02 3.547050e-09 1.424473e-01
## [1] 0.11463147 0.06016293 0.01805218
## [1] 5.571976e-02 1.028217e-10 3.979351e-01
## [1] 7.151026e-02 1.842104e-05 5.483577e-02
## [1] 11
## [1] 8.787581e-02 2.268160e-08 1.590965e-01
## [1] 0.11774749 0.06063067 0.01713328
## [1] 5.353514e-02 2.368737e-07 4.466238e-01
## [1] 6.996312e-02 1.099799e-05 5.383800e-02
## [1] 12
## [1] 0.083671082 0.001950597 0.173586654
## [1] 0.12079154 0.06112418 0.01628896
## [1] 5.153511e-02 5.455009e-08 5.030240e-01
## [1] 6.783213e-02 3.932401e-06 5.373476e-02
## [1] 13
## [1] 0.10435879 0.03044045 0.12933547
## [1] 0.12376846 0.06164877 0.01551085
## [1] 4.969398e-02 6.214053e-07 5.693368e-01
## [1] 6.474426e-02 9.121318e-10 5.526414e-02
## [1] 14
## [1] 0.12375006 0.04414401 0.10145861
## [1] 0.12668498 0.06220659 0.01479127
## [1] 4.799134e-02 3.606712e-07 6.486753e-01
## [1] 6.057496e-02 3.321873e-22 5.930253e-02
## [1] 15
## [1] 0.14170426 0.05065478 0.08265885
## [1] 0.12954332 0.06280333 0.01412459
## [1] 4.640952e-02 2.117025e-07 7.457043e-01
## [1] 5.632374e-02 4.000530e-11 6.500080e-02
## [1] 16
## [1] 0.15834704 0.05357311 0.06924790
## [1] 0.13235179 0.06343758 0.01350456
## [1] 4.493466e-02 4.347401e-07 8.674860e-01
## [1] 5.270441e-02 1.245930e-17 7.097977e-02
## [1] 17
## [1] 0.17388232 0.05467048 0.05923802
## [1] 0.13511342 0.06411299 0.01292688
## [1] 4.355518e-02 9.461703e-07 1.025314e+00
## [1] 0.055032035 0.004495524 0.066694354
## [1] 18
## [1] 0.18852158 0.05484997 0.05148667
## [1] 0.13783430 0.06482927 0.01238710
## [1] 4.226148e-02 2.256846e-06 1.238383e+00
## [1] 0.05844569 0.00918963 0.06112114
## [1] 19
## [1] 0.20245448 0.05458732 0.04530200
## [1] 0.14051724 0.06559108 0.01188215
## [1] 4.104574e-02 1.187742e-05 1.541954e+00
## [1] 0.06175083 0.01322992 0.05633603
## [1] 20
## [1] 0.21584354 0.05413967 0.04024478
## [1] 0.14316867 0.06639255 0.01140798
## [1] 3.990066e-02 2.218728e-05 2.009153e+00
## [1] 0.06496019 0.01676460 0.05217827
## [1] 21
## [1] 0.22882330 0.05364999 0.03602553
## [1] 0.14579506 0.06723397 0.01096163
## [1] 3.882061e-02 6.282879e-05 2.821576e+00
## [1] 0.06808132 0.01990208 0.04853234
## [1] 22
## [1] 0.24151420 0.05319691 0.03244458
## [1] 0.14839778 0.06812189 0.01054148
## [1] 0.0378028370 0.0005923859 4.5907068717
## [1] 0.07112216 0.02272431 0.04530824
## [1] 23
## [1] 0.25401731 0.05282486 0.02936170
## [1] 0.1509789 0.0690567 0.0101454
## [1] 0.03743668 0.18753329 11.36247993
## [1] 0.07409273 0.02529480 0.04243398
## [1] 24
## [1] 0.26642434 0.05255848 0.02667495
## [1] 0.153549668 0.070027105 0.009769895
## [1] 0.2877195 8587.6974183 1226.6704890
## [1] 0.07699792 0.02766200 0.03985604
## [1] 25
## [1] 0.27882036 0.05241070 0.02430849
## [1] 0.156107796 0.071048532 0.009415013
## [1] 199.46330 6208.14224 1.10569
## [1] 0.07984446 0.02986491 0.03753013
## [1] 26
## [1] 0.29128919 0.05238571 0.02220411
## [1] 0.158659014 0.072111490 0.009078167
## [1] 714.462884 10613.716258 0.521378
## [1] 0.08264003 0.03193400 0.03541915
## [1] 27
## [1] 0.30391048 0.05248794 0.02031783
## [1] 0.161210496 0.073222252 0.008758288
## [1] 180.1268381 1762.5832293 0.3392122
## [1] 0.08538688 0.03389469 0.03349618
## [1] 28
## [1] 0.31677033 0.05271618 0.01861413
## [1] 0.163761229 0.074374338 0.008453791
## [1] 6.5735067 45.9612801 0.2408695
## [1] 0.08809358 0.03576662 0.03173475
## [1] 29
## [1] 0.32995732 0.05307150 0.01706533
## [1] 0.166320358 0.075573508 0.008163701
## [1] 3.1097930 16.4575249 0.1814396
## [1] 0.09076424 0.03756685 0.03011545
## [1] 30
## [1] 0.34358251 0.05354613 0.01564706
## [1] 0.168896163 0.076814036 0.007886308
## [1] 2.4251180 10.3315661 0.1448452
## [1] 0.09339814 0.03930999 0.02862384plot(UO, res_LBR2)
UO[which(res_LBR2==min(res_LBR2))]## [1] 72LBR2_res_max <- max(res_LBR2)
plot(UO, res_LBR8)
UO[which(res_LBR8==min(res_LBR8))]## [1] 69LBR8_res_max <- max(res_LBR8)
plot(UO, res_AAVS1)
UO[which(res_AAVS1==min(res_AAVS1))]## [1] 85AAVS1_res_max <- max(res_AAVS1)
plot(UO, res_fLAD)
UO[which(res_fLAD==min(res_fLAD))]## [1] 75fLAD_res_max <- max(res_fLAD)
sum_res <- (res_LBR2/LBR2_res_max)+(res_LBR8/LBR8_res_max)+(res_AAVS1/AAVS1_res_max)+(res_fLAD/fLAD_res_max)
plot(UO, sum_res)
UO[which(sum_res==min(sum_res))]## [1] 76dkc_LBR2 <- density(kc_LBR2)
dkc_LBR8 <- density(kc_LBR8)
dkc_AAVS1 <- density(kc_AAVS1)
dkc_chr11 <- density(kc_chr11)
dkp_LBR2 <- density(kp_LBR2)
dkp_LBR8 <- density(kp_LBR8)
dkp_AAVS1 <- density(kp_AAVS1)
dkp_chr11 <- density(kp_chr11)
dkm_LBR2 <- density(km_LBR2)
dkm_LBR8 <- density(km_LBR8)
dkm_AAVS1 <- density(km_AAVS1)
dkm_chr11 <- density(km_chr11)
plot(dkc_LBR2); plot(dkc_LBR8); plot(dkc_AAVS1); plot(dkc_chr11)
plot(dkp_LBR2); plot(dkp_LBR8); plot(dkp_AAVS1); plot(dkp_chr11)
plot(dkm_LBR2); plot(dkm_LBR8); plot(dkm_AAVS1); plot(dkm_chr11)
###########
time = seq(0,60,0.1);
t = time;
t2 = which(time%in%time_all)
COL=heat_hcl(7)
#LBR2
U_LBR2 <-UO[which(res_LBR2==min(res_LBR2))]
print(U_LBR2)## [1] 72XXX = wt_1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(U_LBR2))/100)
kc_LBR2 = log(2)/lll$hlc
kp_LBR2 = log(2)/(lll$hlm/lll$perfectF)
km_LBR2 = log(2)/lll$hlm
R_sq=(cor(lll$intact[t2][which(is.finite(LBR2_av))],LBR2_av[which(is.finite(LBR2_av))])^2);
print(c(kc_LBR2, kp_LBR2, km_LBR2))## [1] 0.083671082 0.001950597 0.173586654print(log(2)/(kp_LBR2+km_LBR2))## [1] 3.948718print(kp_LBR2/(kp_LBR2+km_LBR2))## [1] 0.01111215#plot(DF[,1],DF[,2],ylim=c(0,100),main='LBR2 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
plot(rownames(wt_1), wt_1[,1],ylim=c(0,100),main='LBR2 fitting', pch=20, col=COL[1], cex=0.5, ylab="intact/total", xlab="time (h)")
lines(rownames(wt_1), wt_1[,2], type="p", col=COL[2],pch=20,cex=0.5)
lines(rownames(wt_1), wt_1[,3], type="p", col=COL[3],pch=20,cex=0.5)
lines(rownames(wt_1), wt_1[,4], type="p", col=COL[4],pch=20,cex=0.5)
lines(rownames(wt_1), wt_1[,5], type="p", col=COL[5],pch=20,cex=0.5)
lines(rownames(wt_1), wt_1[,6], type="p", col=COL[6],pch=20,cex=0.5)
lines(rownames(wt_1), wt_1[,7], type="p", col=COL[7],pch=20,cex=0.5)
lines(time_all, rowMeans(wt_1), type="p", pch=20, cex=1.5, col=2)
lines(time,lll$intact*100,lwd=2,col=2)
text(x=55, y=90, labels=paste("kc = ", formatC(kc_LBR2, digits = 2),"\n", "kp =", formatC(kp_LBR2, digits=2), "\n", "km =", formatC(km_LBR2,digits=2), "\n", "R^2 =", R_sq))
plot(time,lll$broken*100,main='LBR2 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
#LBR8
U_LBR8 <- UO[which(res_LBR8==min(res_LBR8))]
print(U_LBR8)## [1] 69XXX = wt_LBR8;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(U_LBR8))/100)
kc_LBR8 = log(2)/lll$hlc
kp_LBR8 = log(2)/(lll$hlm/lll$perfectF)
km_LBR8 = log(2)/lll$hlm
LBR8_av <- rowMeans(wt_LBR8)
R_sq=(cor(lll$intact[t2][which(is.finite(LBR8_av))],LBR8_av[which(is.finite(LBR8_av))])^2);
print(c(kc_LBR8, kp_LBR8, km_LBR8))## [1] 0.11143445 0.05971845 0.01905680print(log(2)/(kp_LBR8+km_LBR8))## [1] 8.799048print(kp_LBR8/(kp_LBR8+km_LBR8))## [1] 0.7580864#plot(DF[,1],DF[,2],ylim=c(0,100),main='LBR8 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
plot(rownames(wt_LBR8), wt_LBR8[,1],ylim=c(0,100),main='LBR8 fitting', pch=20, col=COL[1], cex=0.5, ylab="intact/total", xlab="time (h)")
lines(rownames(wt_LBR8), wt_LBR8[,2], type="p", col=COL[2],pch=20,cex=0.5)
lines(rownames(wt_LBR8), wt_LBR8[,3], type="p", col=COL[3],pch=20,cex=0.5)
lines(rownames(wt_LBR8), wt_LBR8[,4], type="p", col=COL[4],pch=20,cex=0.5)
lines(rownames(wt_LBR8), wt_LBR8[,5], type="p", col=COL[5],pch=20,cex=0.5)
lines(rownames(wt_LBR8), wt_LBR8[,6], type="p", col=COL[6],pch=20,cex=0.5)
lines(time_all, rowMeans(wt_LBR8), type="p", pch=20, cex=1.5, col=2)
lines(time,lll$intact*100,lwd=2,col=2)
text(x=55, y=90, labels=paste("kc = ", formatC(kc_LBR8, digits = 2),"\n", "kp =", formatC(kp_LBR8, digits=2), "\n", "km =", formatC(km_LBR8,digits=2), "\n", "R^2 =", R_sq))
plot(time,lll$broken*100,main='LBR8 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
#AAVS1
U_AAVS1 <- UO[which(res_AAVS1==min(res_AAVS1))]
print(U_AAVS1)## [1] 85XXX = wt_AAVS1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(U_AAVS1))/100)
kc_AAVS1 = log(2)/lll$hlc
kp_AAVS1 = log(2)/(lll$hlm/lll$perfectF)
km_AAVS1 = log(2)/lll$hlm
R_sq=(cor(lll$intact[t2][which(is.finite(AAVS1_av))],AAVS1_av[which(is.finite(AAVS1_av))])^2);
print(c(kc_AAVS1, kp_AAVS1, km_AAVS1))## [1] 199.46330 6208.14224 1.10569print(log(2)/(kp_AAVS1+km_AAVS1))## [1] 0.0001116314print(kp_AAVS1/(kp_AAVS1+km_AAVS1))## [1] 0.9998219#plot(DF[,1],DF[,2],ylim=c(0,100),main='AAVS1 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
plot(rownames(wt_AAVS1), wt_AAVS1[,1],ylim=c(0,100),main='AAVS1 fitting', pch=20, col=COL[1], cex=0.5, ylab="intact/total", xlab="time (h)")
lines(rownames(wt_AAVS1), wt_AAVS1[,2], type="p", col=COL[2],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_AAVS1[,3], type="p", col=COL[3],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_AAVS1[,4], type="p", col=COL[4],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_AAVS1[,5], type="p", col=COL[5],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_AAVS1[,6], type="p", col=COL[6],pch=20,cex=0.5)
lines(time_all, rowMeans(wt_AAVS1), type="p", pch=20, cex=1.5, col=2)
lines(time,lll$intact*100,lwd=2,col=2)
text(x=55, y=90, labels=paste("kc = ", formatC(kc_AAVS1, digits = 2),"\n", "kp =", formatC(kp_AAVS1, digits=2), "\n", "km =", formatC(km_AAVS1,digits=2), "\n", "R^2 =", R_sq))
plot(time,lll$broken*100,main='AAVS1 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
#chr11
U_fLAD <- UO[which(res_fLAD==min(res_fLAD))]
print(U_fLAD)## [1] 75XXX = wt_chr11;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(U_fLAD))/100)
R_sq=(cor(lll$intact[t2][which(is.finite(chr11_av))],chr11_av[which(is.finite(chr11_av))])^2);
kc_chr11 = log(2)/lll$hlc
kp_chr11 = log(2)/(lll$hlm/lll$perfectF)
km_chr11 = log(2)/lll$hlm
print(c(kc_chr11, kp_chr11, km_chr11))## [1] 5.632374e-02 4.000530e-11 6.500080e-02print(log(2)/(kp_chr11+km_chr11))## [1] 10.66367print(kp_chr11/(kp_chr11+km_chr11))## [1] 6.154585e-10#plot(DF[,1],DF[,2],ylim=c(0,100),main='chr11 fitting',pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
plot(rownames(wt_chr11), wt_chr11[,1],ylim=c(0,100),main='chr11 fitting', pch=20, col=COL[1], cex=0.5, ylab="intact/total", xlab="time (h)")
lines(rownames(wt_chr11), wt_chr11[,2], type="p", col=COL[2],pch=20,cex=0.5)
lines(rownames(wt_chr11), wt_chr11[,3], type="p", col=COL[3],pch=20,cex=0.5)
lines(rownames(wt_chr11), wt_chr11[,4], type="p", col=COL[4],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_chr11[,5], type="p", col=COL[5],pch=20,cex=0.5)
lines(time_all, rowMeans(wt_chr11), type="p", pch=20, cex=1.5, col=2)
lines(time,lll$intact*100,lwd=2,col=2)
text(x=55, y=90, labels=paste("kc = ", formatC(kc_chr11, digits = 2),"\n", "kp =", formatC(kp_chr11, digits=2), "\n", "km =", formatC(km_chr11,digits=2), "\n", "R^2 =", R_sq))
plot(time,lll$broken*100,main='chr11 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
#AAVS1
U_AAVS1 <- mean(c(U_LBR2, U_LBR8, U_fLAD))
print(U_AAVS1)## [1] 72XXX = wt_AAVS1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(100-mean(U_AAVS1))/100)
kc_AAVS1 = log(2)/lll$hlc
kp_AAVS1 = log(2)/(lll$hlm/lll$perfectF)
km_AAVS1 = log(2)/lll$hlm
R_sq=(cor(lll$intact[t2][which(is.finite(AAVS1_av))],AAVS1_av[which(is.finite(AAVS1_av))])^2);
print(c(kc_AAVS1, kp_AAVS1, km_AAVS1))## [1] 5.153511e-02 5.455009e-08 5.030240e-01print(log(2)/(kp_AAVS1+km_AAVS1))## [1] 1.37796print(kp_AAVS1/(kp_AAVS1+km_AAVS1))## [1] 1.084443e-07#plot(DF[,1],DF[,2],ylim=c(0,100),main='AAVS1 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
plot(rownames(wt_AAVS1), wt_AAVS1[,1],ylim=c(0,100),main='AAVS1 fitting', pch=20, col=COL[1], cex=0.5, ylab="intact/total", xlab="time (h)")
lines(rownames(wt_AAVS1), wt_AAVS1[,2], type="p", col=COL[2],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_AAVS1[,3], type="p", col=COL[3],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_AAVS1[,4], type="p", col=COL[4],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_AAVS1[,5], type="p", col=COL[5],pch=20,cex=0.5)
lines(rownames(wt_AAVS1), wt_AAVS1[,6], type="p", col=COL[6],pch=20,cex=0.5)
lines(time_all, rowMeans(wt_AAVS1), type="p", pch=20, cex=1.5, col=2)
lines(time,lll$intact*100,lwd=2,col=2)
text(x=55, y=90, labels=paste("kc = ", formatC(kc_AAVS1, digits = 2),"\n", "kp =", formatC(kp_AAVS1, digits=2), "\n", "km =", formatC(km_AAVS1,digits=2), "\n", "R^2 =", R_sq))
plot(time,lll$broken*100,main='AAVS1 broken', lwd=2,lty=1, pch=16,cex=1.5,col=4,cex.lab=1.5,cex.axis=1.5,xlab='time (h)',ylab='broken (%)', type="l")
bootstrapping
# bootstrap for 1000 times
nt = 1000;
lstfitting = rep(rep(list(NULL),nt),4);
names(lstfitting)=c('LBR2','LBR8','AAVS1','chr11');
#Determined untransfection rate LBR2: 72%, LBR8: 69% and chr11: 75%, AAVS1: 72%.
for (i in 1:nt){
# LBR2
XXX = wt_1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF = DF[sample(1:nrow(DF),size=nrow(DF),replace = TRUE),]
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lstfitting[['LBR2']][[i]] = list(all = ZLWB2fitDefaultAllTP(DF,U=1-0.72))
# LBR8
XXX = wt_LBR8;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF = DF[sample(1:nrow(DF),size=nrow(DF),replace = TRUE),]
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lstfitting[['LBR8']][[i]] = list(all = ZLWB2fitDefaultAllTP(DF, U=1-0.69))
# AAVS1
XXX = wt_AAVS1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF = DF[sample(1:nrow(DF),size=nrow(DF),replace = TRUE),]
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lstfitting[['AAVS1']][[i]] = list(all = ZLWB2fitDefaultAllTP(DF, U=1-0.72))
# chr11
XXX = wt_chr11;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF = DF[sample(1:nrow(DF),size=nrow(DF),replace = TRUE),]
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lstfitting[['chr11']][[i]] = list(all = ZLWB2fitDefaultAllTP(DF, U=1-0.75))
cat(i/nt,'\r')
}
##
time = seq(0,max(time),0.1)
#LBR2 outlier fittered
boxplot(outline=F,log(2)/unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][['hlc']]})),main = 'LBR2 Kc')
aa = mean(unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][['hlc']]})))
legend('topright',legend=paste('mean = ',log(2)/aa))
boxplot(outline=F,log(2)/(unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][['hlm']]}))/unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][[1]]}))),main = 'LBR2 Kp')
bb = mean((unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][['hlm']]}))/unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][[1]]}))))
legend('topright',legend=paste('mean = ',log(2)/bb))
boxplot(outline=F,log(2)/unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][['hlm']]})),main = 'LBR2 Km')
cc = mean(unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][['hlm']]})))
legend('topright',legend=paste('mean = ',log(2)/cc))
XXX = wt_1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(mean(unlist(sapply(lstfitting[['LBR2']],function(x){x[[1]][['Untf']]})))))
idx1 = sapply(lapply(lstfitting[['LBR2']],function(x){x[[1]][['broken']]}),length) == length(seq(0,max(time),0.1))
idx2 = time %in% as.numeric(rownames(XXX))
#intact
plot(DF[,1],DF[,2],ylim=c(0,100),main='LBR2 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
# uncertainty scheme
mtx = matrix(NA,length(time),nt);
rownames(mtx) = as.character(time);
for (i in 1:floor(ncol(mtx)/10)){
if (((sapply(lstfitting[['LBR2']],function(x){length(x[[1]][['intact']])}))==nrow(mtx))[i]){
mtx[,i] = lstfitting[['LBR2']][[i]][[1]][['intact']]
lines(time,mtx[,i]*100,col=rgb(0,0,1,0.2))
}
}
lines(time,lll$intact*100,lwd=2,col=2)
points(DF[,1],DF[,2],ylim=c(0,100),pch=16)
#broken
plot(time,lll$broken*100,lwd=2,col=2,ylim=c(0,100),main='LBR2 broken')
mtx = sapply(lstfitting[['LBR2']][idx1],function(x){x[[1]][['broken']]});
for (i in 1:floor(ncol(mtx)/10)){
lines(time,mtx[,i]*100,col=rgb(0,0,1,0.2))
}
lines(time,lll$broken*100,lwd=2,col=2,ylim=c(0,100))
#LBR8
#LBR8 outlier fittered
boxplot(outline=F,log(2)/unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][['hlc']]})),main = 'LBR8 Kc')
aa = mean(unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][['hlc']]})))
legend('topright',legend=paste('mean = ',log(2)/aa))
boxplot(outline=F,log(2)/(unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][['hlm']]}))/unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][[1]]}))),main = 'LBR8 Kp')
bb = mean((unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][['hlm']]}))/unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][[1]]}))))
legend('topright',legend=paste('mean = ',log(2)/bb))
boxplot(outline=F,log(2)/unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][['hlm']]})),main = 'LBR8 Km')
cc = mean(unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][['hlm']]})))
legend('topright',legend=paste('mean = ',log(2)/cc))
XXX = wt_LBR8;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(mean(unlist(sapply(lstfitting[['LBR8']],function(x){x[[1]][['Untf']]})))))
idx1 = sapply(lapply(lstfitting[['LBR8']],function(x){x[[1]][['broken']]}),length) == length(time)
idx2 = time %in% as.numeric(rownames(XXX))
#intact
plot(DF[,1],DF[,2],ylim=c(0,100),main='LBR8 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
# uncertainty scheme
mtx = matrix(NA,length(time),nt);
rownames(mtx) = as.character(time);
for (i in 1:floor(ncol(mtx)/10)){
if (((sapply(lstfitting[['LBR8']],function(x){length(x[[1]][['intact']])}))==nrow(mtx))[i]){
mtx[,i] = lstfitting[['LBR8']][[i]][[1]][['intact']]
lines(time,mtx[,i]*100,col=rgb(0,0,1,0.2))
}
}
lines(time,lll$intact*100,lwd=2,col=2)
points(DF[,1],DF[,2],ylim=c(0,100),pch=16)
#broken
plot(time,lll$broken*100,lwd=2,col=2,ylim=c(0,100),main='LBR8 broken')
mtx = matrix(unlist(sapply(lstfitting[['LBR8']][idx1],function(x){x[[1]][['broken']]})),nrow=length(time))
for (i in 1:floor(ncol(mtx)/10)){
lines(time,mtx[,i]*100,col=rgb(0,0,1,0.2))
}
lines(time,lll$broken*100,lwd=2,col=2,ylim=c(0,100))
#AAVS1
#AAVS1 outlier fittered
boxplot(outline=F,log(2)/unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['hlc']]})),main = 'AAVS1 Kc')
aa = mean(unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['hlc']]})))
legend('topright',legend=paste('mean = ',log(2)/aa))
boxplot(outline=F,log(2)/(unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['hlm']]}))/unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][[1]]}))),main = 'AAVS1 Kp')
bb = mean((unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['hlm']]}))/unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][[1]]}))))
legend('topright',legend=paste('mean = ',log(2)/bb))
boxplot(outline=F,log(2)/unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['hlm']]})),main = 'AAVS1 Km')
cc = mean(unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['hlm']]})))
legend('topright',legend=paste('mean = ',log(2)/cc))
XXX = wt_AAVS1;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(mean(unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['Untf']]})))))
idx1 = sapply(lapply(lstfitting[['AAVS1']],function(x){x[[1]][['broken']]}),length) == length(time)
idx2 = time %in% as.numeric(rownames(XXX))
#intact
plot(DF[,1],DF[,2],ylim=c(0,100),main='AAVS1 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
# uncertainty scheme
mtx = matrix(NA,length(time),nt);
rownames(mtx) = as.character(time);
for (i in 1:floor(ncol(mtx)/10)){
if (((sapply(lstfitting[['AAVS1']],function(x){length(x[[1]][['intact']])}))==nrow(mtx))[i]){
mtx[,i] = lstfitting[['AAVS1']][[i]][[1]][['intact']]
lines(time,mtx[,i]*100,col=rgb(0,0,1,0.2))
}
}
lines(time,lll$intact*100,lwd=2,col=2)
points(DF[,1],DF[,2],ylim=c(0,100),pch=16)
plot(time,lll$broken*100,lwd=2,col=2,ylim=c(0,100),main='AAVS1 broken')
mtx = matrix(unlist(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['broken']]})[sapply(sapply(lstfitting[['AAVS1']],function(x){x[[1]][['broken']]}),length)==length(time)]),nrow=length(time))
#broken
for (i in 1:floor(ncol(mtx)/10)){
lines(time,mtx[,i]*100,col=rgb(0,0,1,0.2))
}
lines(time,lll$broken*100,lwd=2,col=2,ylim=c(0,100))
#chr11
#chr11 outlier fittered
boxplot(outline=F,log(2)/unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][['hlc']]})),main = 'chr11 Kc')
aa = mean(unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][['hlc']]})))
legend('topright',legend=paste('mean = ',log(2)/aa))
boxplot(outline=F,log(2)/(unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][['hlm']]}))/unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][[1]]}))),main = 'chr11 Kp')
bb = mean((unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][['hlm']]}))/unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][[1]]}))))
legend('topright',legend=paste('mean = ',log(2)/bb))
boxplot(outline=F,log(2)/unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][['hlm']]})),main = 'chr11 Km')
cc = mean(unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][['hlm']]})))
legend('topright',legend=paste('mean = ',log(2)/cc))
XXX = wt_chr11;
DF= data.frame(time = as.numeric(rownames(XXX)),wt = as.numeric(unlist(XXX)),mut = as.numeric(unlist(100-XXX)),pos=rep(seq(1,nrow(XXX)),ncol(XXX)))
DF = DF[!is.na(rowSums(DF)),];
DF[,4]=as.numeric(sapply(DF[,1],function(x){which(sort(unique(DF[,1]))==x)}));
lll = ZLWB2fitDefaultAllTP(DF, U=(mean(unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][['Untf']]})))))
#intact
plot(DF[,1],DF[,2],ylim=c(0,100),main='chr11 fitting', pch=20, col=1, cex=0.5, ylab="intact/total", xlab="time (h)")
# uncertainty scheme
mtx = matrix(NA,length(time),nt);
rownames(mtx) = as.character(time);
for (i in 1:floor(ncol(mtx)/10)){
if (((sapply(lstfitting[['chr11']],function(x){length(x[[1]][['intact']])}))==nrow(mtx))[i]){
mtx[,i] = lstfitting[['chr11']][[i]][[1]][['intact']]
lines(time,mtx[,i]*100,col=rgb(0,0,1,0.2))
}
}
lines(time,lll$intact*100,lwd=2,col=2)
points(DF[,1],DF[,2],ylim=c(0,100),pch=16)
#broken
plot(time,lll$broken*100,lwd=2,col=2,ylim=c(0,100),main='chr11 broken')
mtx = matrix(unlist(sapply(lstfitting[['chr11']],function(x){x[[1]][['broken']]})[sapply(sapply(lstfitting[['chr11']],function(x){x[[1]][['broken']]}),length)==length(time)]),nrow=length(time))
for (i in 1:floor(ncol(mtx)/10)){
lines(time,mtx[,i]*100,col=rgb(0,0,1,0.2))
}
lines(time,lll$broken*100,lwd=2,col=2,ylim=c(0,100))
#combine the bootstrapping results in one big dataset
LBR2_mtx <- cbind(
log(2)/unlist(sapply(lstfitting[['LBR2']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlc']],NA)})),
log(2)/(unlist(sapply(lstfitting[['LBR2']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlm']],NA)}))/unlist(sapply(lstfitting[['LBR2']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['perfectF']],NA)}))),
log(2)/unlist(sapply(lstfitting[['LBR2']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlm']],NA)})),
unlist(sapply(lstfitting[['LBR2']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['maxBro']],NA)})),
unlist(sapply(lstfitting[['LBR2']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['peakBro']],NA)})),
unlist(sapply(lstfitting[['LBR2']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['residual_sq']],NA)})))
colnames(LBR2_mtx) = c('kc','kp','km','maxBro', 'peakBro', 'residual_sq')
LBR8_mtx <- cbind(
log(2)/unlist(sapply(lstfitting[['LBR8']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlc']],NA)})),
log(2)/(unlist(sapply(lstfitting[['LBR8']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlm']],NA)}))/unlist(sapply(lstfitting[['LBR8']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['perfectF']],NA)}))),
log(2)/unlist(sapply(lstfitting[['LBR8']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlm']],NA)})),
unlist(sapply(lstfitting[['LBR8']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['maxBro']],NA)})),
unlist(sapply(lstfitting[['LBR8']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['peakBro']],NA)})),
unlist(sapply(lstfitting[['LBR8']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['residual_sq']],NA)})))
colnames(LBR8_mtx) = c('kc','kp','km','maxBro', 'peakBro', 'residual_sq')
AAVS1_mtx <- cbind(
log(2)/unlist(sapply(lstfitting[['AAVS1']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlc']],NA)})),
log(2)/(unlist(sapply(lstfitting[['AAVS1']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlm']],NA)}))/unlist(sapply(lstfitting[['AAVS1']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['perfectF']],NA)}))),
log(2)/unlist(sapply(lstfitting[['AAVS1']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlm']],NA)})),
unlist(sapply(lstfitting[['AAVS1']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['maxBro']],NA)})),
unlist(sapply(lstfitting[['AAVS1']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['peakBro']],NA)})),
unlist(sapply(lstfitting[['AAVS1']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['residual_sq']],NA)})))
colnames(AAVS1_mtx) = c('kc','kp','km','maxBro', 'peakBro', 'residual_sq')
chr11_mtx <- cbind(
log(2)/unlist(sapply(lstfitting[['chr11']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlc']],NA)})),
log(2)/(unlist(sapply(lstfitting[['chr11']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlm']],NA)}))/unlist(sapply(lstfitting[['chr11']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['perfectF']],NA)}))),
log(2)/unlist(sapply(lstfitting[['chr11']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['hlm']],NA)})),
unlist(sapply(lstfitting[['chr11']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['maxBro']],NA)})),
unlist(sapply(lstfitting[['chr11']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['peakBro']],NA)})),
unlist(sapply(lstfitting[['chr11']],function(x){ifelse(length(x[[1]][['hlm']]),x[[1]][['residual_sq']],NA)})))
colnames(chr11_mtx) = c('kc','kp','km','maxBro', 'peakBro', 'residual_sq')
mtx = list(
LBR2=LBR2_mtx,
LBR8=LBR8_mtx,
AAVS1=AAVS1_mtx,
chr11=chr11_mtx)
df <- mtx
#load("df20180108.rda") # bootstrapping dataset as described in paper
for(i in names(df)) {df[[i]]<-cbind(df[[i]], df[[i]][,2] / (df[[i]][,3]+df[[i]][,2]))
colnames(df[[i]])[7]<-"kp.fraction"
}
for(i in names(df)) {print(apply(df[[i]], 2, quantile, c(0.05, 0.5, 0.95)))} #median & confidence marginsDetermination of the upperbound of p/m and test parameter robustness, on the total mutation scale
Apply functions ZLWB2PiledupOMMerge and ZLWB2PiledupSweepMerge to piled-up series, without NU7441 inhibitor
sweeping from 80% every time shrink a factor of 2 but compensent a factor of i, i is the number of iteration from i to 32
apply F-statistic (p<0.05) to determin upperbound
time = time_all
### optimal fitting for pipled up
optimalParaR = ZLWB2PiledupOMMerge(time,wt_1,indel_1);
optimalPara = optimalParaR[[1]];
upperboundPileupMerge = rep(list(NULL),32);
options(warn=-1)
for (i in 1:length(upperboundPileupMerge)){
fc=0.75;
sweep = fc/(1-fc)*2/(2^i)*i;
upperboundPileupMerge[[i]]=try(ZLWB2PiledupSweepMerge(time,wt_1,indel_1),silent=TRUE)
}
options(warn=1)
# on the total mutation scale
#######################################################
###plot sweeping and residuals
plot(axes=FALSE,log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1)),log2(sapply(upperboundPileupMerge,function(x){x$residual_sq})),pch=16,xlab=bquote('log'[10]*'(k'[p]*' /(k'[p]*'+k'[m]*'))'),ylab=bquote('log'[2]*'(sum of resid'^2*')'),type='o',lwd=3);
points(log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1))[1],log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))[1],cex=1.5,col=3,pch=16)
axis(2)
axis(2,c(-10000,100000000000000))
axis(3,c(-10000,100000000000000))
axis(4,c(-10000,100000000000000))
axis(1,at=seq(-100,100))
abline(v=log10(optimalPara[[1]][3]/(optimalPara[[1]][2]+optimalPara[[1]][3])),lty=2,lwd=3)
abline(h=log2(sum(resid(optimalPara)^2)),lty=2,lwd=3)
#apply f-statistic
df2 = sum(as.numeric(!is.na(indel_1)))-2;
uppershift=log2(sum(resid(optimalPara)^2))+log2(qf(0.9,df2,df2));
abline(h=uppershift,col=2,lty=2,lwd=3)
#solve upperbound
idx2 = max(which(log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))>uppershift));
idx1 = idx2+1;
x1 = log10(sapply(upperboundPileupMerge,function(x){x$perfectF})[idx1]/(1+sapply(upperboundPileupMerge,function(x){x$perfectF})[idx1]));
x2 = log10(sapply(upperboundPileupMerge,function(x){x$perfectF})[idx2]/(1+sapply(upperboundPileupMerge,function(x){x$perfectF})[idx2]));
y1 = log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))[idx1];
y2 = log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))[idx2];
upperbound=(x2-x1)/(y2-y1)*(uppershift-y1)+x1;
abline(v=upperbound,col=2,lty=2,lwd=3);
#labeling
text(srt=90,pos=4,log10(optimalPara[[1]][3]/(optimalPara[[1]][2]+optimalPara[[1]][3])),
as.numeric(crossprod(range(log2(sapply(upperboundPileupMerge,function(x){x$residual}))),c(17/18,1/18))),
paste('op =',as.character(round((optimalPara[[1]][3]/(optimalPara[[1]][2]+optimalPara[[1]][3]))*10000000)/10000000)))
text(srt=90,pos=2,col=2,upperbound
,
as.numeric(crossprod(range(log2(sapply(upperboundPileupMerge,function(x){x$residual}))),c(1/3,2/3))),
paste('ub =',round(10^upperbound,2)));
legend('topleft', legend=c('p = 0.1',paste0('perfect repair = ',fc*100,'%')),bty='n',text.col=c(2,3));
#########
####
#get upperbound fitting
sweep=10^upperbound/(1-10^upperbound);
upperboundfitting=ZLWB2PiledupSweepMerge(time,wt_1,indel_1)
##
###plot sweeping and hlc
plot(axes=FALSE,log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1)),log(2)/sapply(upperboundPileupMerge,function(x){x$hlc}),pch=16,xlab=bquote('log'[10]*'(k'[p]*' /(k'[p]*'+k'[m]*'))'),ylab=expression('k'['c']*'max ( h'^-1*')'),type='o',lwd=3,ylim=c(0,max(log(2)/sapply(upperboundPileupMerge,function(x){x$hlc}))))
axis(2)
axis(2,c(-10000,100000000000000))
axis(3,c(-10000,100000000000000))
axis(4,c(-10000,100000000000000))
axis(1,at=seq(-100,100))
abline(v=log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),lty=2,lwd=3)
abline(h=log(2)/optimalPara[[1]][1],lty=2,lwd=3)
abline(v=upperbound,col=2,lty=2,lwd=3)
#solve upperbound hlc
abline(h=log(2)/upperboundfitting$hlc,col=2,lty=2,lwd=3);
points(log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1))[1],log(2)/sapply(upperboundPileupMerge,function(x){x$hlc})[1],cex=1.5,col=3,pch=16)
#labeling
text(pos=4,log10(optimalPara[[1]][3]/(optimalPara[[1]][2]+optimalPara[[1]][3])),
as.numeric(crossprod(range(log(2)/(sapply(upperboundPileupMerge,function(x){x$hlc}))),c(8/9,1/9))),
bquote('op = '*.(round(log(2)/optimalPara[[1]][1]/10^floor(log10(log(2)/optimalPara[[1]][1])),1)) *'x 10' ^ .(floor(log10(log(2)/optimalPara[[1]][1])))))
text(pos=2,col=2,upperbound
,
as.numeric(crossprod(range(log(2)/(sapply(upperboundPileupMerge,function(x){x$hlc}))),c(3/5,2/5))),
bquote('ub = '*.(round(log(2)/upperboundfitting$hlc/10^floor(log10(log(2)/upperboundfitting$hlc)),1)) *'x 10' ^ .(floor(log10(log(2)/upperboundfitting$hlc)))))
legend('topleft', legend=c('p = 0.1',paste0('perfect repair = ',fc*100,'%')),bty='n',text.col=c(2,3));
#########
##plot sweeping and hlm
plot(axes=FALSE,log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1)),log(2)/sapply(upperboundPileupMerge,function(x){x$hlm}),pch=16,xlab=bquote('log'[10]*'(k'[p]*' /(k'[p]*'+k'[m]*'))'),ylab=expression('k'['m']*' ( h'^-1*')'),type='o',lwd=3,ylim=c(0,max(log(2)/sapply(upperboundPileupMerge,function(x){x$hlm}))))
axis(2)
axis(2,c(-10000,100000000000000))
axis(3,c(-10000,100000000000000))
axis(4,c(-10000,100000000000000))
axis(1,at=seq(-100,100))
abline(v=log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),lty=2,lwd=3)
abline(h=log(2)/optimalPara[[1]][3],lty=2,lwd=3)
abline(v=upperbound,col=2,lty=2,lwd=3)
points(log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1))[1],log(2)/sapply(upperboundPileupMerge,function(x){x$hlm})[1],cex=1.5,col=3,pch=16)
#solve upperbound hlm
#abline(h=upperlevel,col=2,lty=2,lwd=3);
abline(h=log(2)/upperboundfitting$hlm,col=2,lty=2,lwd=3);
#labeling
text(pos=4,log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),
as.numeric(crossprod(range(log(2)/sapply(upperboundPileupMerge,function(x){x$hlm})[sapply(upperboundPileupMerge,function(x){x$hlm})>0]),c(1/2,1/2))),
bquote('op = '*.(round(log(2)/optimalPara[[1]][3]/10^floor(log10(log(2)/optimalPara[[1]][3])),1)) *'x 10' ^ .(floor(log10(log(2)/optimalPara[[1]][3])))))
text(pos=2,col=2,upperbound
,
as.numeric(crossprod(range(log(2)/sapply(upperboundPileupMerge,function(x){x$hlm})[sapply(upperboundPileupMerge,function(x){x$hlm})>0]),c(2/3,1/3))),
bquote('ub = '*.(round(log(2)/upperboundfitting$hlm/10^floor(log10(log(2)/upperboundfitting$hlm)),1)) *'x 10' ^ .(floor(log10(log(2)/upperboundfitting$hlm)))))
legend('topleft', legend=c('p = 0.1',paste0('perfect repair = ',fc*100,'%')),bty='n',text.col=c(2,3));
#########
###plot sweeping and peakBro
plot(axes=FALSE,log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1)),sapply(upperboundPileupMerge,function(x){x$peakBro}),pch=16,xlab=bquote('log'[10]*'(k'[p]*' /(k'[p]*'+k'[m]*'))'),ylab=bquote(tau*' (h)'),type='o',lwd=3,ylim=c(0,max(sapply(upperboundPileupMerge,function(x){x$peakBro}))))
axis(2)
axis(2,c(-10000,100000000000000))
axis(3,c(-10000,100000000000000))
axis(4,c(-10000,100000000000000))
axis(1,at=seq(-100,100))
abline(v=log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),lty=2,lwd=3)
abline(h=optimalParaR[[2]],lty=2,lwd=3)
abline(v=upperbound,col=2,lty=2,lwd=3)
abline(h=upperboundfitting$peakBro,col=2,lty=2,lwd=3);
points(log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1))[1],sapply(upperboundPileupMerge,function(x){x$peakBro})[1],cex=1.5,col=3,pch=16)
#solve upperbound hlc
#labeling
text(pos=4,log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),
as.numeric(crossprod(range((sapply(upperboundPileupMerge,function(x){x$peakBro}))),c(1/9,8/9))),
paste('op =',round(optimalParaR[[2]],1)))
text(pos=2,col=2,upperbound
,
as.numeric(crossprod(range((sapply(upperboundPileupMerge,function(x){x$peakBro}))),c(1/3,2/3))),
paste('ub =',round(upperboundfitting$peakBro,1)));
legend('bottomleft', legend=c('p = 0.1',paste0('perfect repair = ',fc*100,'%')),bty='n',text.col=c(2,3));
#########
###plot sweeping and maxBro
#transfected
AAA = rep(NA,ncol(wt_1));
for (i in 1:ncol(wt_1)){
AAA [i] = (UntransfectedFitting(time,wt_1[,i],indel_1[,i])[1])*100
}
plot(axes=FALSE,log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1)),mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro}),pch=16,xlab=bquote('log'[10]*'(k'[p]*' /(k'[p]*'+k'[m]*'))'),ylab=bquote('max'[broken]*' (%)'),type='o',lwd=3,ylim=c(0,max(mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro}))))
axis(2)
axis(2,c(-10000,100000000000000))
axis(3,c(-10000,100000000000000))
axis(4,c(-10000,100000000000000))
axis(1,at=seq(-100,100))
abline(v=log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),lty=2,lwd=3)
abline(h=mean(AAA)*upperboundPileupMerge[[which.min(abs(sapply(upperboundPileupMerge,function(x){x$perfectF})-optimalPara[[1]][3]/optimalPara[[1]][2]))[1]]][['maxBro']],lty=2,lwd=3)
abline(v=upperbound,col=2,lty=2,lwd=3)
abline(h=upperboundfitting$maxBro*mean(AAA),col=2,lty=2,lwd=3);
points(log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1))[1],mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro})[1],cex=1.5,col=3,pch=16)
#solve upperbound hlc
#labeling
text(pos=4,log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),
as.numeric(crossprod(range((mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro}))),c(1/2,1/2))),
paste('op =',round(mean(AAA)*upperboundPileupMerge[[which.min(abs(sapply(upperboundPileupMerge,function(x){x$perfectF})-optimalPara[[1]][3]/optimalPara[[1]][2]))[1]]][['maxBro']],1)))
text(pos=2,col=2,upperbound
,
as.numeric(crossprod(range((mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro}))),c(1/3,2/3))),
paste('ub =',round(upperboundfitting$maxBro*mean(AAA),1)));
legend('bottomleft', legend=c('p = 0.1',paste0('perfect repair = ',fc*100,'%')),bty='n',text.col=c(2,3));
############################################
#print rates of 3 fits that not significant
print(log(2)/upperboundPileupMerge[[1]]$hlc)## [1] 0.2946564print(log(2)/(upperboundPileupMerge[[1]]$hlm)*upperboundPileupMerge[[1]]$perfectF)## [1] 0.7855851print(log(2)/upperboundPileupMerge[[1]]$hlm)## [1] 0.2618617print(log(2)/upperboundPileupMerge[[3]]$hlc)## [1] 0.2604648print(log(2)/(upperboundPileupMerge[[3]]$hlm)*upperboundPileupMerge[[3]]$perfectF)## [1] 0.3609887print(log(2)/upperboundPileupMerge[[3]]$hlm)## [1] 0.1604394print(log(2)/upperboundPileupMerge[[5]]$hlc)## [1] 0.1840773print(log(2)/(upperboundPileupMerge[[5]]$hlm)*upperboundPileupMerge[[5]]$perfectF)## [1] 0.103171print(log(2)/upperboundPileupMerge[[5]]$hlm)## [1] 0.110049#####
upperboundPileupMerge = rep(list(NULL),13);
options(warn=-1)
for (i in 1:length(upperboundPileupMerge)){
fc=0.99;
sweep = fc/(1-fc)*2/(2^i)*i;
upperboundPileupMerge[[i]]=try(ZLWB2PiledupSweepMerge(time,wt_1,indel_1),silent=TRUE)
}## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 2.44141e-08, R2 = 9.31323e-14
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 2.44141e-08, R2 = 9.31323e-14
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 2.44141e-08, R2 = 9.31323e-14
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 2.44141e-08, R2 = 9.31323e-14
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 2.44141e-08, R2 = 9.31323e-14
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 2.44141e-08, R2 = 9.31323e-14
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 2.44141e-08, R2 = 9.31323e-14
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DINTDY- T (=R1) illegal
## In above message, R1 = 0.1
##
## T not in interval TCUR - HU (= R1) to TCUR (=R2)
## In above message, R1 = 6.76116e-06, R2 = 6.76116e-06
##
## DINTDY- T (=R1) illegal
## In above message, R1 = 0.2
##
## T not in interval TCUR - HU (= R1) to TCUR (=R2)
## In above message, R1 = 6.76116e-06, R2 = 6.76116e-06
##
## DLSODA- Trouble in DINTDY. ITASK = I1, TOUT = R1
## In above message, I1 = 1
##
## In above message, R1 = 0.2
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 3.90625e-07, R2 = 1.49012e-12
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
##
## DLSODA- At T (=R1) and step size H (=R2), the
## corrector convergence failed repeatedly
## or with ABS(H) = HMIN
## In above message, R1 = 9.76563e-08, R2 = 3.72529e-13
## options(warn=0)
upperboundPileupMerge[[6]] <- NULL
###plot sweeping and maxBro
#transfected
AAA = rep(NA,ncol(wt_1));
for (i in 1:ncol(wt_1)){
AAA [i] = (UntransfectedFitting(time,wt_1[,i],indel_1[,i])[1])*100
}
plot(axes=FALSE,log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1)),mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro}),pch=16,xlab=bquote('log'[10]*'(k'[p]*' /(k'[p]*'+k'[m]*'))'),ylab=bquote('max'[broken]*' (%)'),type='o',lwd=3,ylim=c(0,max(mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro}))))
axis(2)
axis(2,c(-10000,100000000000000))
axis(3,c(-10000,100000000000000))
axis(4,c(-10000,100000000000000))
axis(1,c(-10000,100000000000000))
axis(1)
abline(v=log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),lty=2,lwd=3)
abline(h=mean(AAA)*upperboundPileupMerge[[which.min(abs(sapply(upperboundPileupMerge,function(x){x$perfectF})-optimalPara[[1]][3]/optimalPara[[1]][2]))[1]]][['maxBro']],lty=2,lwd=3)
abline(v=upperbound,col=2,lty=2,lwd=3)
abline(h=upperboundfitting$maxBro*mean(AAA),col=2,lty=2,lwd=3);
points(log10(1-1/(sapply(upperboundPileupMerge,function(x){x$perfectF})+1))[1],mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro})[1],cex=1.5,col=3,pch=16)
#solve upperbound hlc
#labeling
text(pos=4,log10(optimalPara[[1]][3]/(optimalPara[[1]][3]+optimalPara[[1]][2])),
as.numeric(crossprod(range((mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro}))),c(1/2,1/2))),
paste('op =',round(mean(AAA)*upperboundPileupMerge[[which.min(abs(sapply(upperboundPileupMerge,function(x){x$perfectF})-optimalPara[[1]][3]/optimalPara[[1]][2]))[1]]][['maxBro']],1)))
text(pos=2,col=2,upperbound
,
as.numeric(crossprod(range((mean(AAA)*sapply(upperboundPileupMerge,function(x){x$maxBro}))),c(1/3,2/3))),
paste('ub =',round(upperboundfitting$maxBro*mean(AAA),1)));
legend('bottomleft', legend=c('p = 0.1',paste0('perfect repair = ',fc*100,'%')),bty='n',text.col=c(2,3));
Determination of the upperbound of kc
:
upperboundPileupMerge = rep(list(NULL),32);
options(warn=-1)
for (i in 1:length(upperboundPileupMerge)){
fc=7.5;
sweep = fc/(1.1^(i-5));
upperboundPileupMerge[[i]]=try(ZLWB2hlcPiledupSweepMerge (time,wt_1,indel_1),silent=TRUE)
}
###plot sweeping and residuals
plot(log10(sapply(upperboundPileupMerge,function(x){x$hlc})),log2(sapply(upperboundPileupMerge,function(x){x$residual_sq})),pch=16,cex=1.5,xlab=bquote('log'[10]*'(k'[c]*')'),ylab=bquote('log'[2]*'(sum of resid'^2*')'),type='o',lwd=3)
abline(v=log10(log(2)/optimalPara[[1]][1]),lty=2,lwd=3)
abline(h=log2(sum(resid(optimalPara)^2)),lty=2,lwd=3)
#apply f-statistic
df2 = sum(as.numeric(!is.na(indel_1)))-2;
uppershift=log2(sum(resid(optimalPara)^2))+log2(qf(0.9,df2,df2));
abline(h=uppershift,col=2,lty=2,lwd=3)
#solve upperbound
idx2 = which(log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))>uppershift)[3];
idx1 = idx2+1;
x1 = log10(sapply(upperboundPileupMerge,function(x){x$hlc}))[idx1];
x2 = log10(sapply(upperboundPileupMerge,function(x){x$hlc}))[idx2];
y1 = log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))[idx1];
y2 = log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))[idx2];
upperbound=(x2-x1)/(y2-y1)*(uppershift-y1)+x1;
upperbound1=upperbound
abline(v=upperbound,col=2,lty=2,lwd=3);
#solve upperbound
idx2 = which(log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))>uppershift)[4];
idx1 = idx2-1;
x1 = log10(sapply(upperboundPileupMerge,function(x){x$hlc}))[idx1];
x2 = log10(sapply(upperboundPileupMerge,function(x){x$hlc}))[idx2];
y1 = log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))[idx1];
y2 = log2(sapply(upperboundPileupMerge,function(x){x$residual_sq}))[idx2];
upperbound=(x2-x1)/(y2-y1)*(uppershift-y1)+x1;
upperbound2=upperbound
abline(v=upperbound,col=2,lty=2,lwd=3);
#labeling
text(srt=90,pos=4,log10(log(2)/optimalPara[[1]][1]),
as.numeric(crossprod(range(log2(sapply(upperboundPileupMerge,function(x){x$residual}))),c(17/18,1/18))),
paste('op =',as.character(round((log(2)/optimalPara[[1]][1])*100)/100)))
text(srt=90,pos=2,col=2,upperbound1
,
as.numeric(crossprod(range(log2(sapply(upperboundPileupMerge,function(x){x$residual}))),c(1/3,2/3))),
paste('ub =',round(10^upperbound1,2)));
text(srt=90,pos=2,col=2,upperbound2
,
as.numeric(crossprod(range(log2(sapply(upperboundPileupMerge,function(x){x$residual}))),c(1/3,2/3))),
paste('ub =',round(10^upperbound2,2)));
legend('topleft', legend='p = 0.1',bty='n',text.col=2);
#########
# peakbro
plot(log10(sapply(upperboundPileupMerge,function(x){x$hlc})),(sapply(upperboundPileupMerge,function(x){x$peakBro})),pch=16,cex=1.5,xlab=bquote('log'[10]*'(k'[c]*')'),ylab='peakBro (h)',type='o',lwd=3)
abline(v=log10(log(2)/optimalPara[[1]][1]),lty=2,lwd=3)
abline(v=upperbound1,col=2,lty=2,lwd=3);
abline(v=upperbound2,col=2,lty=2,lwd=3);
# maxbro
AAA = rep(NA,ncol(wt_1));
for (i in 1:ncol(wt_1)){
AAA [i] = (UntransfectedFitting(time,wt_1[,i],indel_1[,i])[1])*100
}
plot(log10(sapply(upperboundPileupMerge,function(x){x$hlc})),mean(AAA)*(sapply(upperboundPileupMerge,function(x){x$maxBro})),pch=16,cex=1.5,xlab=bquote('log'[10]*'(k'[c]*')'),ylab='maxBro (%)',type='o',lwd=3)
abline(v=log10(log(2)/optimalPara[[1]][1]),lty=2,lwd=3)
#abline(h=mean(AAA)*upperboundPileupMerge[[which.min(abs(sapply(upperboundPileupMerge,function(x){x$perfectF})-optimalPara[[1]][3]/optimalPara[[1]][2]))[1]]][['maxBro']],lty=2,lwd=3)
abline(v=log10(log(2)/optimalPara[[1]][1]),lty=2,lwd=3)
abline(v=upperbound1,col=2,lty=2,lwd=3);
abline(v=upperbound2,col=2,lty=2,lwd=3);
Overlay theoretical predictions with experimental data of broken end fraction kinetics
develop function brokenendPkfit to get peakBro
streching experiemntal data to overlay with therotical predictions
extracting peakBro values from both predictions and experiments, and compare without and with inhibitor treatment
also more meaningful to use polynomial to fit
also use indifidual data series to fit
library(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:Biostrings':
##
## mask, translate## The following object is masked from 'package:BiocGenerics':
##
## combine## The following objects are masked from 'package:plyr':
##
## is.discrete, summarize## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units###function brokenendPkfit
# function brokenendPk to get peakBro
brokenendPkfit = function(time,broken){
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513,1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(par){
b=par[1]
D=Inf
Dp=par[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(par){
b=par[1]
D=Inf
Dp=par[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
par = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=par,lower=rep(0,length(par)))
becy = FL$par;
#####
WB1rxnrate=function(t,c,parm){
# rate constant passed through a list called parms
kc=parm[1]
kp=parm[2]
km=parm[3]
b = becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
fac=parms[4];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=1,B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
B = s[(out[,1]) %in% time,2];
return(B*fac-broken)
#return(B-broken)
}
# function for simulation
WB1rxnrate_sim = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
fac=parms[4];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=1,B=0,M=0),times=t,func=WB1rxnrate,parms=c(kc,kp,km))
s=out[,2:4];
B = s[,2];
return(B*fac)
#return(B)
}
parms=c(3,3,3,8)
FL = try(modFit(f=WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))), silent = TRUE)
return(list(FL,WB1rxnrate_sim(FL[[1]])));
}
## import broken fraction data from agarose gel
timeb <- c(0,4,8,10,12,14,16,18,21,24,33,42,60)
timemin <- c(0, 8,16,24,60)
timeming <- c(62)
##############
#loading data
#############
##no inhibitor
#3prime broken fraction
noinhibitor_broken_end_3prime <- read.delim("noinhibitor_broken_end_3prime.txt", header=FALSE)
noinhibitor_broken_end_3prime <- noinhibitor_broken_end_3prime[,-1];
noinhibitor_broken_end_min_3prime <- read.delim("noinhibitor_broken_end_min_3prime.txt", header=FALSE)
noinhibitor_broken_end_min_3prime <- noinhibitor_broken_end_min_3prime[,-1];
noinhibitor_broken_end_minguide_3prime <- read.delim("noinhibitor_broken_end_minguide_3prime.txt", header=FALSE)
noinhibitor_broken_end_minguide_3prime <- noinhibitor_broken_end_minguide_3prime[,-1];
#3prime input PCR
noinhibitor_broken_end_3prime_inputPCR <- read.delim("noinhibitor_broken_end_3prime_inputPCR.txt", header=FALSE)
noinhibitor_broken_end_3prime_inputPCR <- noinhibitor_broken_end_3prime_inputPCR[,-1];
noinhibitor_broken_end_min_3prime_inputPCR <- read.delim("noinhibitor_broken_end_min_3prime_inputPCR.txt", header=FALSE)
noinhibitor_broken_end_min_3prime_inputPCR <- noinhibitor_broken_end_min_3prime_inputPCR[,-1];
noinhibitor_broken_end_minguide_3prime_inputPCR <- read.delim("noinhibitor_broken_end_minguide_3prime_inputPCR.txt", header=FALSE)
noinhibitor_broken_end_minguide_3prime_inputPCR <- noinhibitor_broken_end_minguide_3prime_inputPCR[,-1];
#5prime broken fraction
noinhibitor_broken_end_5prime <- read.delim("noinhibitor_broken_end_5prime.txt", header=FALSE)
noinhibitor_broken_end_5prime <- noinhibitor_broken_end_5prime[,-1];
noinhibitor_broken_end_min_5prime <- read.delim("noinhibitor_broken_end_min_5prime.txt", header=FALSE)
noinhibitor_broken_end_min_5prime <- noinhibitor_broken_end_min_5prime[,-1];
noinhibitor_broken_end_minguide_5prime <- read.delim("noinhibitor_broken_end_minguide_5prime.txt", header=FALSE)
noinhibitor_broken_end_minguide_5prime <- noinhibitor_broken_end_minguide_5prime[,-1];
#5prime input PCR
noinhibitor_broken_end_5prime_inputPCR <- read.delim("noinhibitor_broken_end_5prime_inputPCR.txt", header=FALSE)
noinhibitor_broken_end_5prime_inputPCR <- noinhibitor_broken_end_5prime_inputPCR[,-1];
noinhibitor_broken_end_min_5prime_inputPCR <- read.delim("noinhibitor_broken_end_min_5prime_inputPCR.txt", header=FALSE)
noinhibitor_broken_end_min_5prime_inputPCR <- noinhibitor_broken_end_min_5prime_inputPCR[,-1];
noinhibitor_broken_end_minguide_5prime_inputPCR <- read.delim("noinhibitor_broken_end_minguide_5prime_inputPCR.txt", header=FALSE)
noinhibitor_broken_end_minguide_5prime_inputPCR <- noinhibitor_broken_end_minguide_5prime_inputPCR[,-1];
##with inhibitor
#3prime broken fracction
withinhibitor_broken_end_3prime <- read.delim("withinhibitor_broken_end_3prime.txt", header=FALSE)
withinhibitor_broken_end_3prime <- withinhibitor_broken_end_3prime[,-1];
withinhibitor_broken_end_min_3prime <- read.delim("withinhibitor_broken_end_min_3prime.txt", header=FALSE)
withinhibitor_broken_end_min_3prime <- withinhibitor_broken_end_min_3prime[,-1];
withinhibitor_broken_end_minguide_3prime <- read.delim("withinhibitor_broken_end_minguide_3prime.txt", header=FALSE)
withinhibitor_broken_end_minguide_3prime <- withinhibitor_broken_end_minguide_3prime[,-1];
#3prime input PCR
withinhibitor_broken_end_3prime_inputPCR <- read.delim("withinhibitor_broken_end_3prime_inputPCR.txt", header=FALSE)
withinhibitor_broken_end_3prime_inputPCR <- withinhibitor_broken_end_3prime_inputPCR[,-1];
withinhibitor_broken_end_min_3prime_inputPCR <- read.delim("withinhibitor_broken_end_min_3prime_inputPCR.txt", header=FALSE)
withinhibitor_broken_end_min_3prime_inputPCR <- withinhibitor_broken_end_min_3prime_inputPCR[,-1];
withinhibitor_broken_end_minguide_3prime_inputPCR <- read.delim("withinhibitor_broken_end_minguide_3prime_inputPCR.txt", header=FALSE)
withinhibitor_broken_end_minguide_3prime_inputPCR <- withinhibitor_broken_end_minguide_3prime_inputPCR[,-1];
##no inhibitor LBR8
#3prime
noinhibitor_broken_end_LBR8_3prime <- read.delim("noinhibitor_broken_end_LBR8_3prime.txt", header=FALSE)
noinhibitor_broken_end_LBR8_3prime <- noinhibitor_broken_end_LBR8_3prime[,-1];
noinhibitor_broken_end_min_LBR8_3prime <- read.delim("noinhibitor_broken_end_min_LBR8_3prime.txt", header=FALSE)
noinhibitor_broken_end_min_LBR8_3prime <- noinhibitor_broken_end_min_LBR8_3prime[,-1];
noinhibitor_broken_end_minguide_LBR8_3prime <- read.delim("noinhibitor_broken_end_minguide_LBR8_3prime.txt", header=FALSE)
noinhibitor_broken_end_minguide_LBR8_3prime <- noinhibitor_broken_end_minguide_LBR8_3prime[,-1];
#5prime
noinhibitor_broken_end_LBR8_5prime <- read.delim("noinhibitor_broken_end_LBR8_5prime.txt", header=FALSE)
noinhibitor_broken_end_LBR8_5prime <- noinhibitor_broken_end_LBR8_5prime[,-1];
noinhibitor_broken_end_min_LBR8_5prime <- read.delim("noinhibitor_broken_end_min_LBR8_5prime.txt", header=FALSE)
noinhibitor_broken_end_min_LBR8_5prime <- noinhibitor_broken_end_min_LBR8_5prime[,-1];
noinhibitor_broken_end_minguide_LBR8_5prime <- read.delim("noinhibitor_broken_end_minguide_LBR8_5prime.txt", header=FALSE)
noinhibitor_broken_end_minguide_LBR8_5prime <- noinhibitor_broken_end_minguide_LBR8_5prime[,-1];
###################
##no inhibitor
#3prime
brokenfplus <- rowMeans(noinhibitor_broken_end_3prime, na.rm = T)
brokenfmin <- rowMeans(noinhibitor_broken_end_min_3prime, na.rm = T)
minguide <- rowMeans(noinhibitor_broken_end_minguide_3prime, na.rm = T)
sdplus <- apply(noinhibitor_broken_end_3prime,1,sd, na.rm = T)
sdmin <- apply(noinhibitor_broken_end_min_3prime, 1,sd, na.rm = T)
sdminguide <- apply(noinhibitor_broken_end_minguide_3prime, 1,sd, na.rm = T)
#5prime
brokenfplus_5 <- rowMeans(noinhibitor_broken_end_5prime, na.rm = T)
brokenfmin_5 <- rowMeans(noinhibitor_broken_end_min_5prime, na.rm = T)
minguide_5 <- rowMeans(noinhibitor_broken_end_minguide_5prime, na.rm = T)
sdplus_5 <- apply(noinhibitor_broken_end_5prime,1,sd, na.rm = T)
sdmin_5 <- apply(noinhibitor_broken_end_min_5prime, 1,sd, na.rm = T)
sdminguide_5 <- apply(noinhibitor_broken_end_minguide_5prime, 1,sd, na.rm = T)
#LBR2 #3+5
brokenfplus3_5 <- rowMeans(cbind(noinhibitor_broken_end_3prime,noinhibitor_broken_end_5prime ), na.rm = T)
brokenfmin3_5 <- rowMeans(cbind(noinhibitor_broken_end_min_3prime,noinhibitor_broken_end_min_5prime), na.rm = T)
minguide3_5 <- rowMeans(cbind(noinhibitor_broken_end_minguide_3prime,noinhibitor_broken_end_minguide_5prime), na.rm = T)
sdplus3_5 <- apply(cbind(noinhibitor_broken_end_3prime,noinhibitor_broken_end_5prime),1,sd, na.rm = T)
sdmin3_5 <- apply(cbind(noinhibitor_broken_end_min_3prime,noinhibitor_broken_end_min_5prime), 1,sd, na.rm = T)
sdminguide3_5 <- apply(cbind(noinhibitor_broken_end_minguide_3prime,noinhibitor_broken_end_minguide_5prime), 1,sd, na.rm = T)
###inputPCR
#3prime
brokenfplus_inputPCR <- rowMeans(noinhibitor_broken_end_3prime_inputPCR, na.rm = T)
brokenfmin_inputPCR <- rowMeans(noinhibitor_broken_end_min_3prime_inputPCR, na.rm = T)
minguide_inputPCR <- rowMeans(noinhibitor_broken_end_minguide_3prime_inputPCR, na.rm = T)
sdplus_inputPCR <- apply(noinhibitor_broken_end_3prime_inputPCR,1,sd, na.rm = T)
sdmin_inputPCR <- apply(noinhibitor_broken_end_min_3prime_inputPCR, 1,sd, na.rm = T)
sdminguide_inputPCR <- apply(noinhibitor_broken_end_minguide_3prime_inputPCR, 1,sd, na.rm = T)
#5prime
brokenfplus_5_inputPCR <- rowMeans(noinhibitor_broken_end_5prime_inputPCR, na.rm = T)
brokenfmin_5_inputPCR <- rowMeans(noinhibitor_broken_end_min_5prime_inputPCR, na.rm = T)
minguide_5_inputPCR <- rowMeans(noinhibitor_broken_end_minguide_5prime_inputPCR, na.rm = T)
sdplus_5_inputPCR <- apply(noinhibitor_broken_end_5prime_inputPCR,1,sd, na.rm = T)
sdmin_5_inputPCR <- apply(noinhibitor_broken_end_min_5prime_inputPCR, 1,sd, na.rm = T)
sdminguide_5_inputPCR <- apply(noinhibitor_broken_end_minguide_5prime_inputPCR, 1,sd, na.rm = T)
##with inhibitor
brokenfplusN <- rowMeans(withinhibitor_broken_end_3prime, na.rm = T)
brokenfminN <- rowMeans(withinhibitor_broken_end_min_3prime, na.rm = T)
minguideN <- rowMeans(withinhibitor_broken_end_minguide_3prime, na.rm = T)
sdplusN <- apply(withinhibitor_broken_end_3prime,1,sd, na.rm = T)
sdminN <- apply(withinhibitor_broken_end_min_3prime, 1,sd, na.rm = T)
sdminguideN <- apply(withinhibitor_broken_end_minguide_3prime, 1,sd, na.rm = T)
#inputPCR
brokenfplusN_inputPCR <- rowMeans(withinhibitor_broken_end_3prime_inputPCR, na.rm = T)
brokenfminN_inputPCR <- rowMeans(withinhibitor_broken_end_min_3prime_inputPCR, na.rm = T)
minguideN_inputPCR <- rowMeans(withinhibitor_broken_end_minguide_3prime_inputPCR, na.rm = T)
sdplusN_inputPCR <- apply(withinhibitor_broken_end_3prime_inputPCR,1,sd, na.rm = T)
sdminN_inputPCR <- apply(withinhibitor_broken_end_min_3prime_inputPCR, 1,sd, na.rm = T)
sdminguideN_inputPCR <- apply(withinhibitor_broken_end_minguide_3prime_inputPCR, 1,sd, na.rm = T)
##########
#no inhibitor
brokenfraction_D <- data.frame(timeb=timeb, brokenf=brokenfplus, sd=sdplus)
brokenfraction_Dm <- data.frame(timemin=timemin, brokenfmin=brokenfmin, sdmin=sdmin)
brokenfraction_Dmg <- data.frame(timeming=timeming, minguide=minguide, sdminguide=sdminguide)
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR2 3prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="a.u")
# fitting the curve
FL=brokenendPkfit(timeb, brokenfraction_D$brokenf);
# add experimental data
with (
data = brokenfraction_D
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Dm
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Dmg
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
abline(v=seq(0,max(timeb),0.1)[which.max(FL[[2]])],col=4,lty=2,lwd=2)
abline(v=optimalParaR[[2]],lty=2,col=rangecolor,lwd=2)
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",'minmax(prediction)'), pch=c(16, 16, 16,17), pt.cex=c(1,1,1,1),col=c(4, "grey", 1,rangecolor), pt.bg =rangecolor, text.col=c(1, 1, 1,1),bty="n")
text(pos=4,l$text$x[1],min(l$text$y)+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL[[2]])],'h',sep=''),col=4)
text(pos=4,l$text$x[1],min(l$text$y)+2*(diff(l$text$y)[1]),paste('peak(prd) =',optimalParaR[[2]],'h',sep=''),col=rangecolor)
noinhibitorPeak = c(seq(0,max(timeb),0.1)[which.max(FL[[2]])],optimalParaR[[2]])
##################################################
#invividual series fitting, expected shape fitting
##################################################
pbe= rep(0,ncol(noinhibitor_broken_end_3prime));
for (i in 1:ncol(noinhibitor_broken_end_3prime)){
Y=noinhibitor_broken_end_3prime[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('w/o NU7441 LBR2 3 prime exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbe[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbe[i],1))),bty='n');
abline(lwd=2,v=pbe[i],lty=2);
}
mean(pbe)## [1] 14.1sd(pbe)## [1] 2.019901##################################################
#average fitting, expected shape fitting
##################################################
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
polygon(sort(rep(c(mean(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}))-sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro})),mean(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}))+sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}))),2)),c(-100,100,100,-100),col=rangecolor,border=rangecolor)
prm=mean(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}));
sdm=sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}));
print(prm)## [1] 16.3print(sdm)## [1] 1.96384#shading
pbe1=mean(pbe)-sd(pbe)
pbe2=mean(pbe)+sd(pbe)
polygon(sort(c(pbe1,pbe2,pbe1,pbe2)),c(-100,100,100,-100),col=rgb(0,0,1,0.5),border=rgb(0,0,1,0.5))
#fit
Y=brokenfraction_D$brokenf
FL=brokenendPkfit(timeb, Y);
# add experimental data
with (
data = brokenfraction_D
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Dm
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Dmg
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",expression(tau['prediction']),expression(tau['measurement'])), pch=c(16, 16, 16,15,15), pt.cex=c(1,1,1,1),col=c(4, "grey", 1,rangecolor,rgb(0,0,1,0.5)), text.col=c(1, 1, 1, 1),bty="n")
##########
#no inhibitor 5 prime
brokenfraction_D5 <- data.frame(timeb=timeb, brokenf=brokenfplus_5, sd=sdplus_5)
brokenfraction_Dm5 <- data.frame(timemin=timemin, brokenfmin=brokenfmin_5, sdmin=sdmin_5)
brokenfraction_Dmg5 <- data.frame(timeming=timeming, minguide=minguide_5, sdminguide=sdminguide_5)
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR2 5prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="tim (h)", ylab="broken (%)")
# fitting the curve
FL=brokenendPkfit(timeb, brokenfraction_D5$brokenf);
# add experimental data
with (
data = brokenfraction_D5
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Dm5
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Dmg5
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
abline(v=seq(0,max(timeb),0.1)[which.max(FL[[2]])],col=4,lty=2,lwd=2)
abline(v=optimalParaR[[2]],lty=2,col=rangecolor,lwd=2)
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",'minmax(prediction)'), pch=c(16, 16, 16,17), pt.cex=c(1,1,1,1),col=c(4, "grey", 1,rangecolor), pt.bg =rangecolor, text.col=c(1, 1, 1,1),bty="n")
text(pos=4,l$text$x[1],min(l$text$y)+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL[[2]])],'h',sep=''),col=4)
text(pos=4,l$text$x[1],min(l$text$y)+2*(diff(l$text$y)[1]),paste('peak(prd) =',optimalParaR[[2]],'h',sep=''),col=rangecolor)
noinhibitorPeak = c(seq(0,max(timeb),0.1)[which.max(FL[[2]])],optimalParaR[[2]])
##################################################
#invividual series fitting, expected shape fitting
##################################################
pbe= rep(0,ncol(noinhibitor_broken_end_5prime));
for (i in 1:ncol(noinhibitor_broken_end_5prime)){
Y=noinhibitor_broken_end_5prime[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('w/o NU7441 LBR2 5prime exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbe[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbe[i],1))),bty='n');
abline(lwd=2,v=pbe[i],lty=2);
}
mean(pbe)## [1] 15.56667sd(pbe)## [1] 2.203028##################################################
#average fitting, expected shape fitting
##################################################
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR2 5prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
polygon(sort(rep(c(mean(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}))-sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro})),mean(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}))+sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}))),2)),c(-100,100,100,-100),col=rangecolor,border=rangecolor)
prm=mean(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}));
sdm=sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}));
print(prm)## [1] 16.3print(sdm)## [1] 1.96384#shading
pbe1=mean(pbe)-sd(pbe)
pbe2=mean(pbe)+sd(pbe)
polygon(sort(c(pbe1,pbe2,pbe1,pbe2)),c(-100,100,100,-100),col=rgb(0,0,1,0.5),border=rgb(0,0,1,0.5))
#fit
Y=brokenfraction_D5$brokenf
FL=brokenendPkfit(timeb, Y);
# add experimental data
with (
data = brokenfraction_D5
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Dm5
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Dmg5
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",expression(tau['prediction']),expression(tau['measurement'])), pch=c(16, 16, 16,15,15), pt.cex=c(1,1,1,1),col=c(4, "grey", 1,rangecolor,rgb(0,0,1,0.5)), text.col=c(1, 1, 1, 1),bty="n")
pbeD=pbe;
########## with inhibitor
brokenfraction_N <- data.frame(timeb=timeb, brokenf=brokenfplusN, sd=sdplusN)
brokenfraction_Nm <- data.frame(timemin=timemin, brokenfmin=brokenfminN, sdmin=sdminN)
brokenfraction_Nmg <- data.frame(timeming=timeming, minguide=minguideN, sdminguide=sdminguideN)
# put on min max data range
rangecolor <- rgb(220,100,0,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(withinhibitor_total_individual_series,function(x){x$broken})),ncol=length(withinhibitor_total_individual_series)),1,range));
plot(main='with NU7441',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="a.u")
# fitting the curve
FL=brokenendPkfit(timeb, brokenfraction_N$brokenf);
# add experimental data
with (
data = brokenfraction_N
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=2,cex=1.5)
)
with (
data = brokenfraction_Nm
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Nmg
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=2);
abline(v=seq(0,max(timeb),0.1)[which.max(FL[[2]])],col=2,lty=2,lwd=2)
opPB =ZLWB2PiledupOMMerge(as.numeric(row.names(wildtype_tot_LBR2)),wt_2,indel_2)[[2]];
abline(v=opPB,lty=2,col=rangecolor,lwd=2)
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",'minmax(prediction)'), pch=c(16, 16, 16,17), pt.cex=c(1,1,1,1),col=c(2, "grey", 1,rangecolor), pt.bg =rangecolor, text.col=c(1, 1, 1,1),bty="n")
text(pos=4,l$text$x[1],min(l$text$y)+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL[[2]])],'h',sep=''),col=2)
text(pos=4,l$text$x[1],min(l$text$y)+2*(diff(l$text$y)[1]),paste('peak(prd) =',opPB,'h',sep=''),col=rangecolor)
withinhibitorPeak = c(seq(0,max(timeb),0.1)[which.max(FL[[2]])],opPB);
##################################################
#individual series fitting, expected shape fitting
######invividual series fitting, expected shape fitting
withinhibitor_broken_end_3prime <- read.delim("withinhibitor_broken_end_3prime.txt", header=FALSE)
withinhibitor_broken_end_3prime = withinhibitor_broken_end_3prime[,-1];
pbe= rep(0,ncol(withinhibitor_broken_end_3prime));
for (i in 1:ncol(withinhibitor_broken_end_3prime)){
Y=withinhibitor_broken_end_3prime[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('with NU7441 exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbe[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbe[i],1))),bty='n');
abline(lwd=2,v=pbe[i],lty=2);
}
mean(pbe)## [1] 15.95sd(pbe)## [1] 0.07071068##################################################
#average fitting, expected shape fitting
##################################################
# put on min max data range
rangecolor <- rgb(220,100,0,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(withinhibitor_total_individual_series,function(x){x$broken})),ncol=length(withinhibitor_total_individual_series)),1,range));
plot(main='with NU7441',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
polygon(sort(rep(c(mean(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}))-sd(sapply(withinhibitor_total_individual_series,function(x){x$peakBro})),mean(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}))+sd(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}))),2)),c(-100,100,100,-100),col=rangecolor,border=rangecolor)
prm=mean(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}));
sdm=sd(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}));
print(prm)## [1] 20.65print(sdm)## [1] 1.195826#shading
pbe1=mean(pbe)-sd(pbe)
pbe2=mean(pbe)+sd(pbe)
polygon(sort(c(pbe1,pbe2,pbe1,pbe2)),c(-100,100,100,-100),col=rgb(1,0,0,0.5),border=rgb(1,0,0,0.5))
#fit
Y=brokenfraction_N$broken
FL=brokenendPkfit(timeb, Y);
# add experimental data
with (
data = brokenfraction_N
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=2,cex=1.5)
)
with (
data = brokenfraction_Nm
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Nmg
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=2);
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",expression(tau['prediction']),expression(tau['measurement'])), pch=c(16, 16, 16,15,15), pt.cex=c(1,1,1,1),col=c(2, "grey", 1,rangecolor,rgb(1,0,0,0.5)), text.col=c(1, 1, 1,1),bty="n")
########################################################################
#+/- inhibitor
########################################################################
# put on min max data range
rangecolor <- rgb(220,100,0,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(withinhibitor_total_individual_series,function(x){x$broken})),ncol=length(withinhibitor_total_individual_series)),1,range));
plot(main='wo/with NU7441',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="a.u")
# fitting the curve
FL=brokenendPkfit(timeb, brokenfraction_D$brokenf);
FL2=brokenendPkfit(timeb, brokenfraction_N$brokenf);
# add experimental data
with (
data = brokenfraction_D
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Dm
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Dmg
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
with (
data = brokenfraction_N
, expr = errbar(timeb, brokenf/max(FL2[[2]]), (brokenf/max(FL2[[2]])+sd/max(FL[[2]])), (brokenf/max(FL2[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=2,cex=1.5)
)
with (
data = brokenfraction_Nm
, expr = errbar(timemin, brokenfmin/max(FL2[[2]]), (brokenfmin/max(FL2[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL2[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col=5,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
abline(v=seq(0,max(timeb),0.1)[which.max(FL[[2]])],col=4,lty=2,lwd=2)
lines(seq(0,max(timeb),0.1),FL2[[2]]/max(FL2[[2]]),col=2);
abline(v=seq(0,max(timeb),0.1)[which.max(FL2[[2]])],col=2,lty=2,lwd=2)
l=legend("topright", legend=c("w/o NU7441 +shield", "w/o NU7441 -shield", "w NU7441 +shield", "w NU7441 -shield", "-guide +shield"), pch=c(16, 16, 16,16, 16), pt.cex=c(1,1,1,1,1),col=c(4, "grey", 2, 5, 1), pt.bg =rangecolor, text.col=c(1, 1, 1,1,1),bty="n")
text(pos=4,l$text$x[1],min(l$text$y)+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL[[2]])],'h',sep=''),col=4)
text(pos=4,l$text$x[1],min(l$text$y)+2+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL2[[2]])],'h',sep=''),col=2)
##################################################
#individual series fitting, expected shape fitting
##################################################
#w/o inhitibotr
pbeD= rep(0,ncol(noinhibitor_broken_end_3prime));
for (i in 1:ncol(noinhibitor_broken_end_3prime)){
Y=noinhibitor_broken_end_3prime[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('w/o NU7441 exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbeD[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbeD[i],1))),bty='n');
abline(lwd=2,v=pbeD[i],lty=2);
}
mean(pbeD)## [1] 14.1sd(pbeD)## [1] 2.019901#with inhitibotr
pbeN= rep(0,ncol(withinhibitor_broken_end_3prime));
for (i in 1:ncol(withinhibitor_broken_end_3prime)){
Y=withinhibitor_broken_end_3prime[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('with NU7441 exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbeN[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbeN[i],1))),bty='n');
abline(lwd=2,v=pbeD[i],lty=2);
}
mean(pbeN)## [1] 15.95sd(pbeN)## [1] 0.07071068##################################################
#average fitting, expected shape fitting
##################################################
# put on min max data range
rangecolor <- rgb(220,100,0,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(withinhibitor_total_individual_series,function(x){x$broken})),ncol=length(withinhibitor_total_individual_series)),1,range));
plot(main='w/o & with NU7441',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
#shading
pbe1=mean(pbeD)-sd(pbeD)
pbe2=mean(pbeD)+sd(pbeD)
polygon(sort(c(pbe1,pbe2,pbe1,pbe2)),c(-100,100,100,-100),col=rgb(0,0,1,0.5),border=rgb(0,0,1,0.5))
#shading
pbeN1=mean(pbeN)-sd(pbeN)
pbeN2=mean(pbeN)+sd(pbeN)
polygon(sort(c(pbeN1,pbeN2,pbeN1,pbeN2)),c(-100,100,100,-100),col=rgb(1,0,0,0.5),border=rgb(1,0,0,0.5))
# fitting the curve
FL=brokenendPkfit(timeb, brokenfraction_D$brokenf);
FL2=brokenendPkfit(timeb, brokenfraction_N$brokenf);
# add experimental data
with (
data = brokenfraction_D
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Dm
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Dmg
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
with (
data = brokenfraction_N
, expr = errbar(timeb, brokenf/max(FL2[[2]]), (brokenf/max(FL2[[2]])+sd/max(FL[[2]])), (brokenf/max(FL2[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=2,cex=1.5)
)
with (
data = brokenfraction_Nm
, expr = errbar(timemin, brokenfmin/max(FL2[[2]]), (brokenfmin/max(FL2[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL2[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col=5,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
lines(seq(0,max(timeb),0.1),FL2[[2]]/max(FL2[[2]]),col=2);
l=legend("topright", legend=c("w/o NU7441 +shield", "w/o NU7441 -shield", "with NU7441 +shield", "with NU7441 -shield", "-guide +shield",expression(tau['w/o measurement']), expression(tau['with measurement'])), pch=c(16, 16, 16,16,16,15,15), pt.cex=c(1,1,1,1,1,1),col=c(4, "grey", 2, 5, 1,rangecolor, rgb(0,0,1,0.5), rangecolor,rgb(1,0,0,0.5)), text.col=c(1, 1, 1,1,1,1),bty="n")
t.test(pbeD, pbeN)##
## Welch Two Sample t-test
##
## data: pbeD and pbeN
## t = -1.8295, df = 3.0147, p-value = 0.1643
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.059209 1.359209
## sample estimates:
## mean of x mean of y
## 14.10 15.95wilcox.test(pbeD, pbeN)##
## Wilcoxon rank sum test
##
## data: pbeD and pbeN
## W = 2, p-value = 0.5333
## alternative hypothesis: true location shift is not equal to 0#####
# barplot
noinhibitorPeak=c(mean(pbeD),mean(sapply(noinhibitor_total_individual_series,function(x){x$peakBro})))
withinhibitorPeak=c(mean(pbeN),mean(sapply(withinhibitor_total_individual_series,function(x){x$peakBro})))
combPeak = t(matrix(c(noinhibitorPeak,withinhibitorPeak),2));
colnames(combPeak )=c('measurement','prediction')
combPeak =combPeak [,c(2,1)];
batp = barplot(combPeak,beside=TRUE,col=c(4,2),ylab=expression(tau* ' (h)'),ylim=c(0,30))
# add error bar
errbar(as.numeric(batp),as.numeric(combPeak),as.numeric(combPeak)-c(sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro})),
sd(sapply(withinhibitor_total_individual_series,function(x){x$peakBro})),
sd(pbeD),sd(pbeN)),as.numeric(combPeak)+c(sd(sapply(noinhibitor_total_individual_series,function(x){x$peakBro})),
sd(sapply(withinhibitor_total_individual_series,function(x){x$peakBro})),
sd(pbeD),sd(pbeN)),add=TRUE,pch='',lwd=2)
legend("topright",
legend = c("w/o NU7441", "with NU7441"),
fill = c(4, 2),bty='n')
###############################################################
#input PCR
###############################################################
#no inhibitor #3 prime
brokenfraction_D_inputPCR <- data.frame(timeb=timeb, brokenf=brokenfplus_inputPCR, sd=sdplus_inputPCR)
brokenfraction_Dm_inputPCR <- data.frame(timemin=timemin, brokenfmin=brokenfmin_inputPCR, sdmin=sdmin_inputPCR)
brokenfraction_Dmg_inputPCR <- data.frame(timeming=timeming, minguide=minguide_inputPCR, sdminguide=sdminguide_inputPCR)
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 input LBR2 3prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="intensity (a.u)")
with (
data = brokenfraction_D_inputPCR
, expr = errbar(timeb, brokenf/max(brokenfraction_D_inputPCR$brokenf), (brokenf/max(brokenfraction_D_inputPCR$brokenf)+sd/max(brokenfraction_D_inputPCR$brokenf)), (brokenf/max(brokenfraction_D_inputPCR$brokenf)-sd/max(brokenfraction_D_inputPCR$brokenf)), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Dm_inputPCR
, expr = errbar(timemin, brokenfmin/max(brokenfraction_D_inputPCR$brokenf), (brokenfmin/max(brokenfraction_D_inputPCR$brokenf)+sdmin/max(brokenfraction_D_inputPCR$brokenf)), (brokenfmin/max(brokenfraction_D_inputPCR$brokenf)-sdmin/max(brokenfraction_D_inputPCR$brokenf)), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Dmg_inputPCR
, expr = errbar(timeming, minguide/max(brokenfraction_D_inputPCR$brokenf), (minguide/max(brokenfraction_D_inputPCR$brokenf)+sdminguide/max(brokenfraction_D_inputPCR$brokenf)), (minguide/max(brokenfraction_D_inputPCR$brokenf)-sdminguide/max(brokenfraction_D_inputPCR$brokenf)), add=T, pch=20, cap=.01, col=1,cex=1.5)
) 
#no inhibitor #5 prime
brokenfraction_D5_inputPCR <- data.frame(timeb=timeb, brokenf=brokenfplus_5_inputPCR, sd=sdplus_5_inputPCR)
brokenfraction_Dm5_inputPCR <- data.frame(timemin=timemin, brokenfmin=brokenfmin_5_inputPCR, sdmin=sdmin_5_inputPCR)
brokenfraction_Dmg5_inputPCR <- data.frame(timeming=timeming, minguide=minguide_5_inputPCR, sdminguide=sdminguide_5_inputPCR)
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 input LBR2 5prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="intensity (a.u)")
with (
data = brokenfraction_D5_inputPCR
, expr = errbar(timeb, brokenf/max(brokenfraction_D5_inputPCR$brokenf), (brokenf/max(brokenfraction_D5_inputPCR$brokenf)+sd/max(brokenfraction_D5_inputPCR$brokenf)), (brokenf/max(brokenfraction_D5_inputPCR$brokenf)-sd/max(brokenfraction_D5_inputPCR$brokenf)), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Dm5_inputPCR
, expr = errbar(timemin, brokenfmin/max(brokenfraction_D5_inputPCR$brokenf), (brokenfmin/max(brokenfraction_D5_inputPCR$brokenf)+sdmin/max(brokenfraction_D5_inputPCR$brokenf)), (brokenfmin/max(brokenfraction_D5_inputPCR$brokenf)-sdmin/max(brokenfraction_D5_inputPCR$brokenf)), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Dmg5_inputPCR
, expr = errbar(timeming, minguide/max(brokenfraction_D5_inputPCR$brokenf), (minguide/max(brokenfraction_D5_inputPCR$brokenf)+sdminguide/max(brokenfraction_D5_inputPCR$brokenf)), (minguide/max(brokenfraction_D5_inputPCR$brokenf)-sdminguide/max(brokenfraction_D5_inputPCR$brokenf)), add=T, pch=20, cap=.01, col=1,cex=1.5)
) 
#with inhibitor #3 prime
brokenfraction_N_inputPCR <- data.frame(timeb=timeb, brokenf=brokenfplusN_inputPCR, sd=sdplusN_inputPCR)
brokenfraction_Nm_inputPCR <- data.frame(timemin=timemin, brokenfmin=brokenfminN_inputPCR, sdmin=sdminN_inputPCR)
brokenfraction_Nmg_inputPCR <- data.frame(timeming=timeming, minguide=minguideN_inputPCR, sdminguide=sdminguideN_inputPCR)
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(withinhibitor_total_individual_series,function(x){x$broken})),ncol=length(withinhibitor_total_individual_series)),1,range));
plot(main='NU7441 input 3prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="intensity (a.u)")
with (
data = brokenfraction_N_inputPCR
, expr = errbar(timeb, brokenf/max(brokenfraction_N_inputPCR$brokenf), (brokenf/max(brokenfraction_N_inputPCR$brokenf)+sd/max(brokenfraction_N_inputPCR$brokenf)), (brokenf/max(brokenfraction_N_inputPCR$brokenf)-sd/max(brokenfraction_N_inputPCR$brokenf)), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_Nm_inputPCR
, expr = errbar(timemin, brokenfmin/max(brokenfraction_N_inputPCR$brokenf), (brokenfmin/max(brokenfraction_N_inputPCR$brokenf)+sdmin/max(brokenfraction_N_inputPCR$brokenf)), (brokenfmin/max(brokenfraction_N_inputPCR$brokenf)-sdmin/max(brokenfraction_N_inputPCR$brokenf)), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_Nmg_inputPCR
, expr = errbar(timeming, minguide/max(brokenfraction_N_inputPCR$brokenf), (minguide/max(brokenfraction_N_inputPCR$brokenf)+sdminguide/max(brokenfraction_N_inputPCR$brokenf)), (minguide/max(brokenfraction_N_inputPCR$brokenf)-sdminguide/max(brokenfraction_N_inputPCR$brokenf)), add=T, pch=20, cap=.01, col=1,cex=1.5)
) 
##############################################################
#LBR8
#############################################################
#3prime
brokenfplus8 <- rowMeans(noinhibitor_broken_end_LBR8_3prime, na.rm = T)
brokenfmin8 <- rowMeans(noinhibitor_broken_end_min_LBR8_3prime, na.rm = T)
minguide8 <- rowMeans(noinhibitor_broken_end_minguide_LBR8_3prime, na.rm = T)
sdplus8 <- apply(noinhibitor_broken_end_LBR8_3prime,1,sd, na.rm = T)
sdmin8 <- apply(noinhibitor_broken_end_min_LBR8_3prime, 1,sd, na.rm = T)
sdminguide8 <- apply(noinhibitor_broken_end_minguide_LBR8_3prime, 1,sd, na.rm = T)
#5prime
brokenfplus8_5 <- rowMeans(noinhibitor_broken_end_LBR8_5prime, na.rm = T)
brokenfmin8_5 <- rowMeans(noinhibitor_broken_end_min_LBR8_5prime, na.rm = T)
minguide8_5 <- rowMeans(noinhibitor_broken_end_minguide_LBR8_5prime, na.rm = T)
sdplus8_5 <- apply(noinhibitor_broken_end_LBR8_5prime,1,sd, na.rm = T)
sdmin8_5 <- apply(noinhibitor_broken_end_min_LBR8_5prime, 1,sd, na.rm = T)
sdminguide8_5 <- apply(noinhibitor_broken_end_minguide_LBR8_5prime, 1,sd, na.rm = T)
#LBR8 #3+5
brokenfplus8_3_5 <- rowMeans(cbind(noinhibitor_broken_end_LBR8_3prime,noinhibitor_broken_end_LBR8_5prime ), na.rm = T)
brokenfmin8_3_5 <- rowMeans(cbind(noinhibitor_broken_end_min_LBR8_3prime,noinhibitor_broken_end_min_LBR8_5prime), na.rm = T)
minguide8_3_5 <- rowMeans(cbind(noinhibitor_broken_end_minguide_LBR8_3prime,noinhibitor_broken_end_minguide_LBR8_5prime), na.rm = T)
sdplus8_3_5 <- apply(cbind(noinhibitor_broken_end_LBR8_3prime,noinhibitor_broken_end_LBR8_5prime),1,sd, na.rm = T)
sdmin8_3_5 <- apply(cbind(noinhibitor_broken_end_min_LBR8_3prime,noinhibitor_broken_end_min_LBR8_5prime), 1,sd, na.rm = T)
sdminguide8_3_5 <- apply(cbind(noinhibitor_broken_end_minguide_LBR8_3prime,noinhibitor_broken_end_minguide_LBR8_5prime), 1,sd, na.rm = T)
###################
#3prime
brokenfraction_8 <- data.frame(timeb=timeb, brokenf=brokenfplus8, sd=sdplus8)
brokenfraction_8m <- data.frame(timemin=timemin, brokenfmin=brokenfmin8, sdmin=sdmin8)
brokenfraction_8mg <- data.frame(timeming=timeming, minguide=minguide8, sdminguide=sdminguide8)
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR8 3prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="tim (h)", ylab="broken (%)")
# fitting the curve
FL=brokenendPkfit(timeb, brokenfraction_8$brokenf);
# add experimental data
with (
data = brokenfraction_8
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_8m
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_8mg
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
abline(v=seq(0,max(timeb),0.1)[which.max(FL[[2]])],col=4,lty=2,lwd=2)
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield"), pch=c(16, 16, 16), pt.cex=c(1,1,1),col=c(4, "grey", 1), pt.bg =rangecolor, text.col=c(1, 1, 1),bty="n")
text(pos=4,l$text$x[1],min(l$text$y)+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL[[2]])],'h',sep=''),col=4)
##################################################
#invividual series fitting, expected shape fitting
##################################################
pbe= rep(0,ncol(noinhibitor_broken_end_LBR8_3prime));
for (i in 1:ncol(noinhibitor_broken_end_LBR8_3prime)){
Y=noinhibitor_broken_end_LBR8_3prime[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('w/o NU7441 LBR8 3prime exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbe[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbe[i],1))),bty='n');
abline(lwd=2,v=pbe[i],lty=2);
}
mean(pbe)## [1] 34.43333sd(pbe)## [1] 22.64516##################################################
#average fitting, expected shape fitting
##################################################
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR8 3prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
#shading
pbe1=mean(pbe)-sd(pbe)
pbe2=mean(pbe)+sd(pbe)
polygon(sort(c(pbe1,pbe2,pbe1,pbe2)),c(-100,100,100,-100),col=rgb(0,0,1,0.5),border=rgb(0,0,1,0.5))
#fit
Y=brokenfraction_8$brokenf
FL=brokenendPkfit(timeb, Y)
# add experimental data
with (
data = brokenfraction_8
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_8m
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_8mg
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",expression(tau['measurement'])), pch=c(16, 16, 16,15), pt.cex=c(1,1,1),col=c(4, "grey", 1,rangecolor,rgb(0,0,1,0.5)), text.col=c(1, 1, 1),bty="n")
##########
#no inhibitor 5 prime
brokenfraction_85 <- data.frame(timeb=timeb, brokenf=brokenfplus_5, sd=sdplus_5)
brokenfraction_8m5 <- data.frame(timemin=timemin, brokenfmin=brokenfmin_5, sdmin=sdmin_5)
brokenfraction_8mg5 <- data.frame(timeming=timeming, minguide=minguide_5, sdminguide=sdminguide_5)
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR8 5prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="tim (h)", ylab="broken (%)")
# fitting the curve
FL=brokenendPkfit(timeb, brokenfraction_85$brokenf);
# add experimental data
with (
data = brokenfraction_85
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_8m5
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_8mg5
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
abline(v=seq(0,max(timeb),0.1)[which.max(FL[[2]])],col=4,lty=2,lwd=2)
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",'minmax(prediction)'), pch=c(16, 16, 16,17), pt.cex=c(1,1,1,1),col=c(4, "grey", 1,rangecolor), pt.bg =rangecolor, text.col=c(1, 1, 1,1),bty="n")
text(pos=4,l$text$x[1],min(l$text$y)+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL[[2]])],'h',sep=''),col=4)
##################################################
#invividual series fitting, expected shape fitting
##################################################
pbe= rep(0,ncol(noinhibitor_broken_end_LBR8_5prime));
for (i in 1:ncol(noinhibitor_broken_end_LBR8_5prime)){
Y=noinhibitor_broken_end_LBR8_5prime[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('w/o NU7441 lBR8 5prime exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbe[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbe[i],1))),bty='n');
abline(lwd=2,v=pbe[i],lty=2);
}
mean(pbe)## [1] 20.55sd(pbe)## [1] 3.747666##################################################
#average fitting, expected shape fitting
##################################################
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR8 5prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
#shading
pbe1=mean(pbe)-sd(pbe)
pbe2=mean(pbe)+sd(pbe)
polygon(sort(c(pbe1,pbe2,pbe1,pbe2)),c(-100,100,100,-100),col=rgb(0,0,1,0.5),border=rgb(0,0,1,0.5))
#fit
Y=brokenfraction_85$brokenf
FL=brokenendPkfit(timeb, Y);
# add experimental data
with (
data = brokenfraction_85
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_8m5
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_8mg5
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
l=legend("topright", legend=c("+shield", "-shield", "-guide +shield",expression(tau['measurement'])), pch=c(16, 16, 16,15), pt.cex=c(1,1,1),col=c(4, "grey", 1,rangecolor,rgb(0,0,1,0.5)), text.col=c(1, 1, 1),bty="n")
######
#LBR2 vs LBR8
brokenfraction_35 <- data.frame(timeb=timeb, brokenf=brokenfplus3_5, sd=sdplus3_5)
brokenfraction_m35 <- data.frame(timemin=timemin, brokenfmin=brokenfmin3_5, sdmin=sdmin3_5)
brokenfraction_mg35 <- data.frame(timeming=timeming, minguide=minguide3_5, sdminguide=sdminguide3_5)
brokenfraction8_35 <- data.frame(timeb=timeb, brokenf=brokenfplus8_3_5, sd=sdplus8_3_5)
brokenfraction8_m35 <- data.frame(timemin=timemin, brokenfmin=brokenfmin8_3_5, sdmin=sdmin8_3_5)
brokenfraction8_mg35 <- data.frame(timeming=timeming, minguide=minguide8_3_5, sdminguide=sdminguide8_3_5)
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR2 & LBR8 3&5prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="tim (h)", ylab="broken (%)")
# fitting the curve
FL=brokenendPkfit(timeb, brokenfraction_35$brokenf);
FL2=brokenendPkfit(timeb, brokenfraction8_35$brokenf);
# add experimental data
with (
data = brokenfraction_35
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_m35
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_mg35
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
with (
data = brokenfraction8_35
, expr = errbar(timeb, brokenf/max(FL2[[2]]), (brokenf/max(FL2[[2]])+sd/max(FL2[[2]])), (brokenf/max(FL2[[2]])-sd/max(FL2[[2]])), add=T, pch=20, cap=.01, col=2,cex=1.5)
)
with (
data = brokenfraction8_m35
, expr = errbar(timemin, brokenfmin/max(FL2[[2]]), (brokenfmin/max(FL2[[2]])+sdmin/max(FL2[[2]])), (brokenfmin/max(FL2[[2]])-sdmin/max(FL2[[2]])), add=T, pch=20, cap=.01, col=5,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
abline(v=seq(0,max(timeb),0.1)[which.max(FL[[2]])],col=4,lty=2,lwd=2)
l=legend("topright", legend=c("+shield lBR2", "-shield lBR2", "+shield lBR8", "-shield lBR8", "-guide +shield"), pch=c(16, 16, 16,16, 16), pt.cex=c(1,1,1, 1),col=c(4, "grey",2, 5, 1), text.col=c(1, 1, 1, 1),bty="n")
text(pos=4,l$text$x[1],min(l$text$y)+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL[[2]])],'h',sep=''),col=4)
lines(seq(0,max(timeb),0.1),FL2[[2]]/max(FL2[[2]]),col=2);
abline(v=seq(0,max(timeb),0.1)[which.max(FL2[[2]])],col=2,lty=2,lwd=2)
text(pos=4,l$text$x[1],min(l$text$y)+(diff(l$text$y)[1]),paste('peak(exp) =',seq(0,max(timeb),0.1)[which.max(FL2[[2]])],'h',sep=''),col=5)
##################################################
#invividual series fitting, expected shape fitting
##################################################
#lbr2
pbe= rep(0,ncol(cbind(noinhibitor_broken_end_3prime,noinhibitor_broken_end_5prime)));
for (i in 1:ncol(cbind(noinhibitor_broken_end_3prime,noinhibitor_broken_end_5prime))){
Y=cbind(noinhibitor_broken_end_3prime,noinhibitor_broken_end_5prime)[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('w/o NU7441 lBR2 3&5prime exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbe[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbe[i],1))),bty='n');
abline(lwd=2,v=pbe[i],lty=2);
}
#lbr8
pbe8= rep(0,ncol(cbind(noinhibitor_broken_end_LBR8_3prime,noinhibitor_broken_end_LBR8_5prime)));
for (i in 1:ncol(cbind(noinhibitor_broken_end_LBR8_3prime,noinhibitor_broken_end_LBR8_5prime))){
Y=cbind(noinhibitor_broken_end_LBR8_3prime,noinhibitor_broken_end_LBR8_5prime)[,i];
FL=brokenendPkfit(timeb[is.finite(timeb+Y)], Y[is.finite(timeb+Y)]);
plot(main=paste('w/o NU7441 lBR8 3&5prime exp',i),timeb,Y/max(FL[[2]]),pch=16,xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]));
pbe8[i]=seq(0,max(timeb),0.1)[which.max(FL[[2]])];
legend('topright',legend=bquote(tau == .(round(pbe8[i],1))),bty='n');
abline(lwd=2,v=pbe8[i],lty=2);
}
t.test(pbe, pbe8)##
## Welch Two Sample t-test
##
## data: pbe and pbe8
## t = -1.7668, df = 4.077, p-value = 0.1507
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -36.225771 7.922914
## sample estimates:
## mean of x mean of y
## 14.72857 28.88000wilcox.test(pbe, pbe8)##
## Wilcoxon rank sum test
##
## data: pbe and pbe8
## W = 1, p-value = 0.005051
## alternative hypothesis: true location shift is not equal to 0##################################################
#average fitting, expected shape fitting
##################################################
# put on min max data range
rangecolor <- rgb(30,144,255,alpha=80,maxColorValue=255)
minmaxbro=t(apply(matrix(unlist(lapply(noinhibitor_total_individual_series,function(x){x$broken})),ncol=length(noinhibitor_total_individual_series)),1,range));
plot(main='w/o NU7441 LBR2 & LBR8 3&5prime',seq(0,max(timeb),0.1),minmaxbro[,2]*100,col='white',type='l',lwd=4,ylim=c(-0.05,1.5),xlim=range(c(timeb,timeming)),xlab="time (h)", ylab="broken fraction (a.u.)")
#shading
pbe1=mean(pbe)-sd(pbe)
pbe2=mean(pbe)+sd(pbe)
polygon(sort(c(pbe1,pbe2,pbe1,pbe2)),c(-100,100,100,-100),col=rgb(0,0,1,0.5),border=rgb(0,0,1,0.5))
pbe81=mean(pbe8)-sd(pbe8)
pbe82=mean(pbe8)+sd(pbe8)
polygon(sort(c(pbe81,pbe82,pbe81,pbe82)),c(-100,100,100,-100),col=rgb(1,0,0,0.5),border=rgb(1,0,0,0.5))
#fit
Y=brokenfraction_35$brokenf
FL=brokenendPkfit(timeb, Y);
Y2=brokenfraction8_35$brokenf
FL2=brokenendPkfit(timeb, Y2);
with (
data = brokenfraction_35
, expr = errbar(timeb, brokenf/max(FL[[2]]), (brokenf/max(FL[[2]])+sd/max(FL[[2]])), (brokenf/max(FL[[2]])-sd/max(FL[[2]])), add=T, pch=20, cap=.01, col=4,cex=1.5)
)
with (
data = brokenfraction_m35
, expr = errbar(timemin, brokenfmin/max(FL[[2]]), (brokenfmin/max(FL[[2]])+sdmin/max(FL[[2]])), (brokenfmin/max(FL[[2]])-sdmin/max(FL[[2]])), add=T, pch=20, cap=.01, col="grey",cex=1.5)
)
with (
data = brokenfraction_mg35
, expr = errbar(timeming, minguide/max(FL[[2]]), (minguide/max(FL[[2]])+sdminguide/max(FL[[2]])), (minguide/max(FL[[2]])-sdminguide/max(FL[[2]])), add=T, pch=20, cap=.01, col=1,cex=1.5)
)
with (
data = brokenfraction8_35
, expr = errbar(timeb, brokenf/max(FL2[[2]]), (brokenf/max(FL2[[2]])+sd/max(FL2[[2]])), (brokenf/max(FL2[[2]])-sd/max(FL2[[2]])), add=T, pch=20, cap=.01, col=2,cex=1.5)
)
with (
data = brokenfraction8_m35
, expr = errbar(timemin, brokenfmin/max(FL2[[2]]), (brokenfmin/max(FL2[[2]])+sdmin/max(FL2[[2]])), (brokenfmin/max(FL2[[2]])-sdmin/max(FL2[[2]])), add=T, pch=20, cap=.01, col=5,cex=1.5)
)
lines(seq(0,max(timeb),0.1),FL[[2]]/max(FL[[2]]),col=4);
lines(seq(0,max(timeb),0.1),FL2[[2]]/max(FL2[[2]]),col=2);
l=legend("topright", legend=c("+shield lBR2", "-shield lBR2", "+shield lBR8", "-shield lBR8", "-guide +shield",expression(tau['measurement LBR2']), expression(tau['measurement LBR2'])), pch=c(16, 16, 16,16, 16, 15, 15), pt.cex=c(1,1,1, 1,1,1),col=c(4, "grey",2, 5, 1,rangecolor,rgb(0,0,1,0.5), rangecolor,rgb(0,0,1,0.5)), text.col=c(1, 1, 1, 1, 1, 1),bty="n")
pbeD=pbe;Modeling individual series to break down into +1, -7 and other indels
Apply function ZLWB2fitDefault to individual series, spliting into without and with NU7441 inhibitor
# loading data
intact_LBR2 <- read.delim("intact_LBR2.txt", stringsAsFactors=FALSE);
deletion7_LBR2 <- read.delim("deletion7_LBR2.txt", stringsAsFactors=FALSE);
insertion1_LBR2 <- read.delim("insertion1_LBR2.txt", stringsAsFactors=FALSE);
othermut_LBR2 <- read.delim("othermut_LBR2.txt", stringsAsFactors=FALSE);
# remove the sigmoid fitting data
intact_LBR2 = intact_LBR2[-nrow(intact_LBR2),];
deletion7_LBR2 = deletion7_LBR2[-nrow(deletion7_LBR2),];
insertion1_LBR2 = insertion1_LBR2[-nrow(insertion1_LBR2),];
othermut_LBR2 = othermut_LBR2[-nrow(othermut_LBR2),];
# extract time point information
time = as.numeric(row.names(wildtype_tot_LBR2));
#without inhibitor
wt_3= intact_LBR2[,-c(4,6,8,10,12)]
indel_3= 100-intact_LBR2[,-c(4,6,8,10,12)]
plus1_3=insertion1_LBR2[,-c(4,6,8,10,12)]
minus7_3=deletion7_LBR2[,-c(4,6,8,10,12)]
#with inhibitor
wt_4= intact_LBR2[,c(4,6,8,10)]
indel_4= 100-intact_LBR2[,c(4,6,8,10)]
plus1_4=insertion1_LBR2[,c(4,6,8,10)]
minus7_4=deletion7_LBR2[,c(4,6,8,10)]
# fitting for noinhibitor
#################################################################
R_sq=NA
R_sq_plus1=NA
R_sq_minus7=NA
noinhibitor_total_individual_series_split = rep(list(NULL),ncol(wt_3));
for (i in 1:ncol(wt_3)){
noinhibitor_total_individual_series_split[[i]]=ZLWB2fit_MutantsDefault(time,wt_3[,i],indel_3[,i],plus1_3[,i],minus7_3[,i]);
AA = UntransfectedFitting(time,wt_3[,i],indel_3[,i]);
# data
plot(time,wt_3[,i],main=paste(colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
# untransfected
plot(time,wt_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
lines(seq(0,60,0.01),(1-AA[1]/(1 + exp(-AA[2] * (seq(0,60,0.01)-AA[3]))))*100,lwd=3);
legend('topright',paste('U =',round(100-AA[1]*100),'%'),bty='n')
# fitting intact
plot(time,wt_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)',col=2);
lines(time[which(is.finite(wt_3[,i]))],noinhibitor_total_individual_series_split[[i]]$intact*100,lwd=3,col=2)
R_sq[i]=cor(noinhibitor_total_individual_series_split[[i]]$intact,wt_3[which(is.finite(wt_3[,i])),i])^2;
kcmax= log(2)/noinhibitor_total_individual_series_split[[i]]$hlc;
kp= log(2)/(noinhibitor_total_individual_series_split[[i]]$hlm/noinhibitor_total_individual_series_split[[i]]$perfectF);
km=log(2)/noinhibitor_total_individual_series_split[[i]]$hlm;
kpl1=log(2)/noinhibitor_total_individual_series_split[[i]]$hlpl1;
kmn7=log(2)/noinhibitor_total_individual_series_split[[i]]$hlmn7;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting mutants
plot(time,indel_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='mutated/(intact+mutated) (%)',xlab='time (h)',col=3);
lines(time[which(is.finite(wt_3[,i]))],noinhibitor_total_individual_series_split[[i]]$mutant*100,lwd=3,col=3)
R_sq[i]=cor(noinhibitor_total_individual_series_split[[i]]$mutant,indel_3[which(is.finite(wt_3[,i])),i])^2
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting +1
plot(time,plus1_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='+1/(intact+mutated) (%)',xlab='time (h)',col=5);
lines(time[which(is.finite(wt_3[,i]))],noinhibitor_total_individual_series_split[[i]]$plus1*100,lwd=3,col=5)
R_sq_plus1[i]=cor(noinhibitor_total_individual_series_split[[i]]$plus1,plus1_3[which(is.finite(wt_3[,i])),i])^2
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq_plus1[i],2))), bty='n')
# plotting -7
plot(time,minus7_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='-7/(intact+mutated) (%)',xlab='time (h)',col=6);
lines(time[which(is.finite(wt_3[,i]))],noinhibitor_total_individual_series_split[[i]]$minus7*100,lwd=3,col=6)
R_sq_minus7[i]=cor(noinhibitor_total_individual_series_split[[i]]$minus7,minus7_3[which(is.finite(wt_3[,i])),i])^2
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq_minus7[i],2))), bty='n')
# plotting broken
plot(seq(0,max(time),0.1),main=paste('w/o NU7441:',colnames(wt_3)[i]),noinhibitor_total_individual_series_split[[i]]$broken*100,lwd=3,col=4,type='l',ylab='broken (%)',xlab='time (h)')
abline(v=noinhibitor_total_individual_series_split[[i]]$peakBro,col=4,lwd=3,lty=2)
abline(h=0,col=4,lwd=3,lty=2)
legend('topright',legend=bquote(tau == .(noinhibitor_total_individual_series_split[[i]]$peakBro)~' h '), bty='n')
#plot rates
bplt=barplot(main=paste('w/o NU7441:',colnames(wt_3)[i]),c(kcmax,kp,km,kpl1,kmn7),
col=c(4,1,2,5,6),names.arg=
c(expression('k'['c']*'max'),
expression('k'['p']),
expression('k'['m']),
expression('k'['+1']),
expression('k'['-7'])
),horiz=TRUE,las=2,xlab=expression('h'^-1));
rg=range(c(kcmax,kp,km,kpl1,kmn7));
text(mean(rg),bplt[1,1],bquote(.(round(kcmax/10^floor(log10(kcmax)),1)) *'x 10' ^ .(floor(log10(kcmax)))),col=3,cex=1.5)
text(mean(rg),bplt[2,1],bquote(.(round(kp/10^floor(log10(kp)),1)) *'x 10' ^ .(floor(log10(kp)))),col=3,cex=1.5)
text(mean(rg),bplt[3,1],bquote(.(round(km/10^floor(log10(km)),1)) *'x 10' ^ .(floor(log10(km)))),col=3,cex=1.5)
text(mean(rg),bplt[4,1],bquote(.(round(kpl1/10^floor(log10(kpl1)),1)) *'x 10' ^ .(floor(log10(kpl1)))),col=3,cex=1.5)
text(mean(rg),bplt[5,1],bquote(.(round(kmn7/10^floor(log10(kmn7)),1)) *'x 10' ^ .(floor(log10(kmn7)))),col=3,cex=1.5)
}
print(R_sq)## [1] 0.9966992 0.9963770 0.9978009 0.9975188 0.9946609 0.9960952 0.9850133print(mean(R_sq))## [1] 0.9948808print(sd(R_sq))## [1] 0.004470691print(R_sq_plus1)## [1] 0.9848927 0.9925569 0.9970744 0.9932542 0.9948108 0.9958257 0.9930528print(mean(R_sq_plus1))## [1] 0.9930668print(sd(R_sq_plus1))## [1] 0.003954413print(R_sq_minus7)## [1] 0.8725281 0.9173948 0.9696018 0.9300328 0.9831669 0.9236575 0.8990175print(mean(R_sq_minus7))## [1] 0.9279142print(sd(R_sq_minus7))## [1] 0.03835497#################################################################
# fitting for withinhibitor
#################################################################
R_sq_i=NA
R_sq_i_plus1=NA
R_sq_i_minus7=NA
withinhibitor_total_individual_series_split = rep(list(NULL),ncol(wt_4));
for (i in 1:ncol(wt_4)){
withinhibitor_total_individual_series_split[[i]]=ZLWB2fit_MutantsDefault(time,wt_4[,i],indel_4[,i],plus1_4[,i],minus7_4[,i]);
AA = UntransfectedFitting(time,wt_4[,i],indel_4[,i]);
# data
plot(time,wt_4[,i],main=paste(colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
# untransfected
plot(time,wt_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
lines(seq(0,60,0.01),(1-AA[1]/(1 + exp(-AA[2] * (seq(0,60,0.01)-AA[3]))))*100,lwd=3);
legend('topright',paste('U =',round(100-AA[1]*100),'%'),bty='n')
# fitting intact
plot(time,wt_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)',col=2);
lines(time[which(is.finite(wt_4[,i]))],withinhibitor_total_individual_series_split[[i]]$intact*100,lwd=3,col=2)
R_sq_i[i]=cor(withinhibitor_total_individual_series_split[[i]]$intact,wt_4[which(is.finite(wt_4[,i])),i])^2;
kcmax= log(2)/withinhibitor_total_individual_series_split[[i]]$hlc;
kp= log(2)/(withinhibitor_total_individual_series_split[[i]]$hlm/withinhibitor_total_individual_series_split[[i]]$perfectF);
km=log(2)/withinhibitor_total_individual_series_split[[i]]$hlm;
kpl1=log(2)/withinhibitor_total_individual_series_split[[i]]$hlpl1;
kmn7=log(2)/withinhibitor_total_individual_series_split[[i]]$hlmn7;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting mutants
plot(time,indel_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='mutated/(intact+mutated) (%)',xlab='time (h)',col=3);
lines(time[which(is.finite(wt_4[,i]))],withinhibitor_total_individual_series_split[[i]]$mutant*100,lwd=3,col=3)
R_sq_i[i]=cor(withinhibitor_total_individual_series_split[[i]]$mutant,indel_4[which(is.finite(wt_4[,i])),i])^2;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq_i[i],2))), bty='n')
# plotting +1
plot(time,plus1_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='+1/(intact+mutated) (%)',xlab='time (h)',col=5);
lines(time[which(is.finite(wt_4[,i]))],withinhibitor_total_individual_series_split[[i]]$plus1*100,lwd=3,col=5)
R_sq_i_plus1[i]=cor(withinhibitor_total_individual_series_split[[i]]$plus1,plus1_4[which(is.finite(wt_4[,i])),i])^2;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq_i_plus1[i],2))), bty='n')
# plotting -7
plot(time,minus7_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='-7/(intact+mutated) (%)',xlab='time (h)',col=6);
lines(time[which(is.finite(wt_4[,i]))],withinhibitor_total_individual_series_split[[i]]$minus7*100,lwd=3,col=6)
R_sq_i_minus7[i]=cor(withinhibitor_total_individual_series_split[[i]]$minus7,minus7_4[which(is.finite(wt_4[,i])),i])^2
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq_i_minus7[i],2))), bty='n')
# plotting broken
plot(seq(0,max(time),0.1),main=paste('with NU7441:',colnames(wt_4)[i]),withinhibitor_total_individual_series_split[[i]]$broken*100,lwd=3,col=4,type='l',ylab='broken (%)',xlab='time (h)')
abline(v=withinhibitor_total_individual_series_split[[i]]$peakBro,col=4,lwd=3,lty=2)
abline(h=0,col=4,lwd=3,lty=2)
legend('topright',legend=bquote(tau == .(withinhibitor_total_individual_series_split[[i]]$peakBro)~' h '), bty='n')
#plot rates
bplt=barplot(main=paste('with NU7441:',colnames(wt_4)[i]),c(kcmax,kp,km,kpl1,kmn7),
col=c(4,1,2,5,6),names.arg=
c(expression('k'['c']*'max'),
expression('k'['p']),
expression('k'['m']),
expression('k'['+1']),
expression('k'['-7'])
),horiz=TRUE,las=2,xlab=expression('h'^-1));
rg=range(c(kcmax,kp,km,kpl1,kmn7));
text(mean(rg),bplt[1,1],bquote(.(round(kcmax/10^floor(log10(kcmax)),1)) *'x 10' ^ .(floor(log10(kcmax)))),col=3,cex=1.5)
text(mean(rg),bplt[2,1],bquote(.(round(kp/10^floor(log10(kp)),1)) *'x 10' ^ .(floor(log10(kp)))),col=3,cex=1.5)
text(mean(rg),bplt[3,1],bquote(.(round(km/10^floor(log10(km)),1)) *'x 10' ^ .(floor(log10(km)))),col=3,cex=1.5)
text(mean(rg),bplt[4,1],bquote(.(round(kpl1/10^floor(log10(kpl1)),1)) *'x 10' ^ .(floor(log10(kpl1)))),col=3,cex=1.5)
text(mean(rg),bplt[5,1],bquote(.(round(kmn7/10^floor(log10(kmn7)),1)) *'x 10' ^ .(floor(log10(kmn7)))),col=3,cex=1.5)
}
print(R_sq_i)## [1] 0.9962235 0.9943826 0.9958234 0.9929136print(mean(R_sq_i))## [1] 0.9948358print(sd(R_sq_i))## [1] 0.001505704print(R_sq_i_plus1)## [1] 0.9850709 0.9595959 0.9989625 0.9634333print(mean(R_sq_i_plus1))## [1] 0.9767656print(sd(R_sq_i_plus1))## [1] 0.01856724print(R_sq_i_minus7)## [1] 0.9892574 0.9838242 0.9903082 0.9861013print(mean(R_sq_i_minus7))## [1] 0.9873728print(sd(R_sq_i_minus7))## [1] 0.002965191Collect individual data series and represent parameters, +1 and -7
split into without and with NU7441
library(beeswarm);
# hlc
beeswarm(list(log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlc}),log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlc})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['c']*'max'*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(noinhibitor_total_individual_series_split,function(x){x$hlc})),1),'±',round(sd(sapply(noinhibitor_total_individual_series_split,function(x){x$hlc})),1)),paste(round(mean(sapply(withinhibitor_total_individual_series_split,function(x){x$hlc})),1),'+/-',round(sd(sapply(withinhibitor_total_individual_series_split,function(x){x$hlc})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlc});
d2 = log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlc});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
# hlp
beeswarm(list(log(2)/(sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(noinhibitor_total_individual_series_split,function(x){x$perfectF})),log(2)/(sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(withinhibitor_total_individual_series_split,function(x){x$perfectF}))),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['p']*' (h'^-1*')'));
lgd = legend('bottomright',c(paste(round(mean(log(2)/(sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(noinhibitor_total_individual_series_split,function(x){x$perfectF}))),2),'+/-',round(sd(log(2)/(sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(noinhibitor_total_individual_series_split,function(x){x$perfectF}))),2)),paste(round(mean(log(2)/(sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(withinhibitor_total_individual_series_split,function(x){x$perfectF}))),2),'+/-',round(sd(log(2)/(sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(withinhibitor_total_individual_series_split,function(x){x$perfectF}))),2))),text.col=c(0,0),bty='n')
d1 = (log(2)/(sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(noinhibitor_total_individual_series_split,function(x){x$perfectF})));
d2 = (log(2)/(sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(withinhibitor_total_individual_series_split,function(x){x$perfectF})));
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
# hlm
beeswarm(list(log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlm}),log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['m']*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})),1),'±',round(sd(sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})),1)),paste(round(mean(sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})),1),'+/-',round(sd(sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlm});
d2 = log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlm});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
# hlpl1
beeswarm(list(log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlpl1}),log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlpl1})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['+1']*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(noinhibitor_total_individual_series_split,function(x){x$hlpl1})),1),'±',round(sd(sapply(noinhibitor_total_individual_series_split,function(x){x$hlpl1})),1)),paste(round(mean(sapply(withinhibitor_total_individual_series_split,function(x){x$hlpl1})),1),'+/-',round(sd(sapply(withinhibitor_total_individual_series_split,function(x){x$hlpl1})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlpl1});
d2 = log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlpl1});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
# hlmn7
beeswarm(list(log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlmn7}),log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlmn7})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['-7']*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(noinhibitor_total_individual_series_split,function(x){x$hlmn7})),1),'±',round(sd(sapply(noinhibitor_total_individual_series_split,function(x){x$hlmn7})),1)),paste(round(mean(sapply(withinhibitor_total_individual_series_split,function(x){x$hlmn7})),1),'+/-',round(sd(sapply(withinhibitor_total_individual_series_split,function(x){x$hlmn7})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlmn7});
d2 = log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlmn7});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
# peakBro
beeswarm(list(sapply(noinhibitor_total_individual_series_split,function(x){x$peakBro}),sapply(withinhibitor_total_individual_series_split,function(x){x$peakBro})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=bquote(tau * ' (h)' ))
legend('bottomright',c(paste(round(mean(sapply(noinhibitor_total_individual_series_split,function(x){x$peakBro})),1),'+/-',round(sd(sapply(noinhibitor_total_individual_series_split,function(x){x$peakBro})),1)),paste(round(mean(sapply(withinhibitor_total_individual_series_split,function(x){x$peakBro})),1),'+/-',round(sd(sapply(withinhibitor_total_individual_series_split,function(x){x$peakBro})),1))),text.col=c(4,2),bty='n')
Compare combinated mutation model and individual mutation model
split into without and with NU7441
Compare common values for hlc, hlp, hlm and peakBro
####without inhibitor
# hlc
beeswarm(main='w/o NU7441',list(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlc})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['c']*'max'*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlc}),method='spearman'),2))),bty='n')
# hlp
beeswarm(main='w/o NU7441',list(log(2)/(sapply(noinhibitor_total_individual_series,function(x){x$hlm})/sapply(noinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(noinhibitor_total_individual_series_split,function(x){x$perfectF}))),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['p']*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/(sapply(noinhibitor_total_individual_series,function(x){x$hlm})/sapply(noinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(noinhibitor_total_individual_series_split,function(x){x$perfectF})),method='spearman'),2))),bty='n')
# hlm
beeswarm(main='w/o NU7441',list(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlm})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['m']*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(noinhibitor_total_individual_series_split,function(x){x$hlm}),method='spearman'),2))),bty='n')
# peakBro
beeswarm(main='w/o NU7441',list(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}),sapply(noinhibitor_total_individual_series_split,function(x){x$peakBro})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression(tau *' (h)'))
legend('topleft',legend=bquote(rho==.(round(cor(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}),sapply(noinhibitor_total_individual_series_split,function(x){x$peakBro}),method='spearman'),2))),bty='n')
####without inhibitor
# hlc
beeswarm(main='with NU7441',list(log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlc})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['c']*'max'*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlc}),method='spearman'),2))),bty='n')
# hlp
beeswarm(main='with NU7441',list(log(2)/(sapply(withinhibitor_total_individual_series,function(x){x$hlm})/sapply(withinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(withinhibitor_total_individual_series_split,function(x){x$perfectF}))),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['p']*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/(sapply(withinhibitor_total_individual_series,function(x){x$hlm})/sapply(withinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})/sapply(withinhibitor_total_individual_series_split,function(x){x$perfectF})),method='spearman'),2))),bty='n')
# hlm
beeswarm(main='with NU7441',list(log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlm})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['m']*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(withinhibitor_total_individual_series_split,function(x){x$hlm}),method='spearman'),2))),bty='n')
# peakBro
beeswarm(main='with NU7441',list(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}),sapply(withinhibitor_total_individual_series_split,function(x){x$peakBro})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression(tau *' (h)'))
legend('topleft',legend=bquote(rho==.(round(cor(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}),sapply(withinhibitor_total_individual_series_split,function(x){x$peakBro}),method='spearman'),2))),bty='n')
Compare combinated indel model and individual indel model
split into without and with NU7441
Compare R square in terms of total, +1 and -7
# R square comparison
library(beeswarm);
# function R_sq caculator
R2calculator = function(x,y){
vec = rep(0,ncol(y));
for (i in 1:ncol(y)){
vec[i] = cor(x[!is.na(x[,i]),i],y[,i])^2
}
return(vec)
}
rg=c(0.85,1);
# without inhibitor
beeswarm(main='w/o NU7441',list(R2calculator(indel_1,sapply(noinhibitor_total_individual_series,function(x){x$mutant})),R2calculator(indel_3,sapply(noinhibitor_total_individual_series_split,function(x){x$mutant})),R2calculator(plus1_3,sapply(noinhibitor_total_individual_series_split,function(x){x$plus1})),R2calculator(minus7_3,sapply(noinhibitor_total_individual_series_split,function(x){x$minus7})))
,pch=c(16,16,17,18),cex=1.5,col=c(6,3,3,3),labels = c("total", "total","+1","-7"),ylab=expression('R'^2),ylim=rg)
legend('bottomleft',c(paste(round(mean(R2calculator(indel_1,sapply(noinhibitor_total_individual_series,function(x){x$mutant}))),2),'+/-',round(sd(R2calculator(indel_1,sapply(noinhibitor_total_individual_series,function(x){x$mutant}))),2))
,paste(round(mean(R2calculator(indel_3,sapply(noinhibitor_total_individual_series_split,function(x){x$mutant}))),2),'+/-',round(sd(R2calculator(indel_3,sapply(noinhibitor_total_individual_series_split,function(x){x$mutant}))),2)),
paste(round(mean(R2calculator(plus1_3,sapply(noinhibitor_total_individual_series_split,function(x){x$plus1}))),2),'+/-',round(sd(R2calculator(plus1_3,sapply(noinhibitor_total_individual_series_split,function(x){x$plus1}))),2)),
paste(round(mean(R2calculator(minus7_3,sapply(noinhibitor_total_individual_series_split,function(x){x$minus7}))),2),'+/-',round(sd(R2calculator(minus7_3,sapply(noinhibitor_total_individual_series_split,function(x){x$minus7}))),2))
),pch=c(16,16,17,18),col=c(6,3,3,3),bty='n')
legend('bottomright',c('fitting combined mutation','fitting individual mutation'),text.col=c(6,3),bty='n')
# with inhibitor
beeswarm(main='with NU7441',list(R2calculator(indel_2,sapply(withinhibitor_total_individual_series,function(x){x$mutant})),R2calculator(indel_4,sapply(withinhibitor_total_individual_series_split,function(x){x$mutant})),R2calculator(plus1_4,sapply(withinhibitor_total_individual_series_split,function(x){x$plus1})),R2calculator(minus7_4,sapply(withinhibitor_total_individual_series_split,function(x){x$minus7})))
,pch=c(16,16,17,18),cex=1.5,col=c(6,3,3,3),labels = c("total", "total","+1","-7"),ylab=expression('R'^2),ylim=rg)
legend('bottomleft',c(paste(round(mean(R2calculator(indel_2,sapply(withinhibitor_total_individual_series,function(x){x$mutant}))),2),'+/-',round(sd(R2calculator(indel_2,sapply(withinhibitor_total_individual_series,function(x){x$mutant}))),2))
,paste(round(mean(R2calculator(indel_4,sapply(withinhibitor_total_individual_series_split,function(x){x$mutant}))),2),'+/-',round(sd(R2calculator(indel_4,sapply(withinhibitor_total_individual_series_split,function(x){x$mutant}))),2)),
paste(round(mean(R2calculator(plus1_4,sapply(withinhibitor_total_individual_series_split,function(x){x$plus1}))),2),'+/-',round(sd(R2calculator(plus1_4,sapply(withinhibitor_total_individual_series_split,function(x){x$plus1}))),2)),
paste(round(mean(R2calculator(minus7_4,sapply(withinhibitor_total_individual_series_split,function(x){x$minus7}))),2),'+/-',round(sd(R2calculator(minus7_4,sapply(withinhibitor_total_individual_series_split,function(x){x$minus7}))),2))
),pch=c(16,16,17,18),col=c(6,3,3,3),bty='n')
legend('bottomright',c('fitting combined mutation','fitting individual mutation'),text.col=c(6,3),bty='n')
Develop an accelerating model for minus7 which is MMEJ pathway
split into without and with NU7441
Develop function ZLWB2fit_MutantsDefaultMinus7ab: assume linear build up of activity of -7 rate
Fit to individual indels, a complete model for optimal values of kc kp and km, kplus1, kminus7a and kminusb, with all predictions stored and returned
##################################################################################
# ZLWB2fit_MutantsDefaultMinus7ab: fitting to individual indels, a complete model for optimal values of kc kp and km, kplus1, kminus7a and kminusb, with all predictions stored and returned
##################################################################################
ZLWB2fit_MutantsDefaultMinus7ab = function(time,wt,indel,plus1,minus7){
# fitting untransfected
x=time
y=wt/100
z=indel/100
is1 = plus1/100;
mn7 = minus7/100;
index = which(is.finite(x+y+z));
x = x[index];
y = y[index];
z = z[index];
is1 = is1[index];
mn7= mn7[index];
fitmodel <- nls(y~(1-a/(1 + exp(-b * (x-c)))), start=list(a=25,b=1,c=1))
U = 1-coef(fitmodel)[1]
untransf=U
t= seq(0,max(time*10))/10;
### cas9 fitting
# Cas9 protein kinetics
tWB = c(60,42,33,24,21,18,16,14,12,10,8,4,0)
Cas9I = c(1.035116259,0.93119759,1.134895008,0.980514232,0.989879665,0.903886513, 1.211372653,0.575621105,0.782199246,0.686693037,0.608119422,0.325573414,0.142920737)
Cas9WIfit=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(c(coI)-c(Cas9))
}
Cas9WISim=function(parms){
b=parms[1]
D=Inf
Dp=parms[2]
Cas9=Cas9I;
coI=b/Dp+(b/D-b/Dp)*exp(-Dp*tWB)
return(list(coI))
}
parms = c(1,0.1);
FL= modFit(f= Cas9WIfit,p=parms,lower=rep(0,length(parms)))
becy = FL$par;
listkinetc=list(NULL);
WB1rxnrate=function(t,c,parms){
# rate constant passed through a list called parms
kc=parms[1];
kp=parms[2];
km=parms[3];
kplus1 = parms[4]
kminus7min = parms[5];
kminus7max = parms[6];
b = becy[1];
D=Inf;
Dp=becy[2];
# c is the concentration of species
# derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=-kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp+kp*c["B"] #dcA/dt
r[2]=kc*c["P"]*(b/Dp+(b/D-b/Dp)*exp(-Dp*t))/b*Dp-(kp+km)*c["B"] #dcB/dt
r[3]=km*c["B"] #dcC/dt
r[4]=kplus1*c["B"]#dcC/dt
r[5]=((kminus7max-kminus7min)/max(time)*t+kminus7min)*c["B"]
# the computed derivatives are returned as a list
# order of derivatives needs to be the same as the order of species in c
return(list(r))
}
# function for fitting results
WB1rxnrate_fit = function(parms){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
kplus1 = log(2)/parms[4]
kminus7min = log(2)/parms[5];
kminus7max = log(2)/parms[6];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0,PL1=0,MN7=0),times=t,func=WB1rxnrate,parms=c(kc,kp,km,kplus1,kminus7min,kminus7max))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
return(c(M/(U+P+M)-df[,3],PL1/(U+P+M)-df[,4],MN7/(U+P+M)-df[,5]))
}
# function for repair results
rxnrate_repair = function(parms,U){
tmax = max(time);
kc=log(2)/parms[1];
kp=log(2)/parms[2];
km=log(2)/parms[3];
kplus1 = log(2)/parms[4]
kminus7min = log(2)/parms[5];
kminus7max = log(2)/parms[6];
b = becy[1];
D=Inf;
Dp=becy[2];
out=ode(y=c(P=as.numeric(1-U),B=0,M=0,PL1=0,MN7=0),times=t,func=WB1rxnrate,parms=c(kc,kp,km,kplus1,kminus7min,kminus7max))
s=out[,2:6];
P = s[(out[,1]) %in% x,1];
M= s[(out[,1]) %in% x,3];
PL1= s[(out[,1]) %in% x,4];
MN7= s[(out[,1]) %in% x,5];
return(list(s[,2],(P+U)/(P+U+M),M/(P+U+M),PL1/(U+P+M),MN7/(U+P+M)))
}
# fitting
df= data.frame(time=x, wt=y*100, indel=z*100, plus1= is1*100, minus7=mn7*100)
df[,1]=df[,1]-df[1,1]
df[,2:5]=df[,2:5]/100
t= seq(0,max(df[,1])*10)/10
parms=c(8,8,8,8,8,8)
FL= try(modFit(f= WB1rxnrate_fit,p=parms,lower=rep(0,length(parms))),silent=TRUE)
if (length(FL)>1){
residuals=resid (FL)
listkinetc=list( perfectF = FL$par[3]/FL$par[2],
hlc = FL$par[1],
hlm = FL$par[3],
hlpl1 = FL$par[4],
hlmn7a = FL$par[5],
hlmn7b = FL$par[6],
intact=rxnrate_repair(FL$par,U)[[2]],
broken=rxnrate_repair(FL$par,U)[[1]],
maxBro = max(rxnrate_repair(FL$par,U)[[1]]),
peakBro =seq(0,max(time),0.1)[which.max(rxnrate_repair(FL$par,U)[[1]])],
mutant=rxnrate_repair(FL$par,U)[[3]],
plus1=rxnrate_repair(FL$par,U)[[4]],
minus7=rxnrate_repair(FL$par,U)[[5]],
residual_sq=sum(residuals^2));
}
return(listkinetc);
}
##################################################################################Modeling individual series to break down into +1, -7a and -7b, combined model
Apply function ZLWB2fit_MutantsDefaultMinus7ab to individual series, spliting into without and with NU7441 inhibitor
# fitting for noinhibitor
#################################################################
R_sq=NA
R_sq_plus1=NA
R_sq_minus7=NA
combined_noinhibitor_nonlinearminus7 = rep(list(NULL),ncol(wt_3));
for (i in 1:ncol(wt_3)){
combined_noinhibitor_nonlinearminus7[[i]]=ZLWB2fit_MutantsDefaultMinus7ab(time,wt_3[,i],indel_3[,i],plus1_3[,i],minus7_3[,i]);
AA = UntransfectedFitting(time,wt_3[,i],indel_3[,i]);
# data
plot(time,wt_3[,i],main=paste(colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
# untransfected
plot(time,wt_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
lines(seq(0,60,0.01),(1-AA[1]/(1 + exp(-AA[2] * (seq(0,60,0.01)-AA[3]))))*100,lwd=3);
legend('topright',paste('U =',round(100-AA[1]*100),'%'),bty='n')
# fitting intact
plot(time,wt_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)',col=2);
lines(time[which(is.finite(wt_3[,i]))],combined_noinhibitor_nonlinearminus7[[i]]$intact*100,lwd=3,col=2)
R_sq[i]=cor(combined_noinhibitor_nonlinearminus7[[i]]$intact,wt_3[which(is.finite(wt_3[,i])),i])^2;
kcmax= log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlc;
kp= log(2)/(combined_noinhibitor_nonlinearminus7[[i]]$hlm/combined_noinhibitor_nonlinearminus7[[i]]$perfectF);
km=log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlm;
kpl1=log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlpl1;
kmn7a=log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7a;
kmn7b=log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7b;
legend('bottomright',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting mutants
plot(time,indel_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='mutated/(intact+mutated) (%)',xlab='time (h)',col=3);
lines(time[which(is.finite(wt_3[,i]))],combined_noinhibitor_nonlinearminus7[[i]]$mutant*100,lwd=3,col=3)
R_sq[i]=cor(combined_noinhibitor_nonlinearminus7[[i]]$mutant,indel_3[which(is.finite(wt_3[,i])),i])^2
legend('bottomright',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting +1
plot(time,plus1_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='+1/(intact+mutated) (%)',xlab='time (h)',col=5);
lines(time[which(is.finite(wt_3[,i]))],combined_noinhibitor_nonlinearminus7[[i]]$plus1*100,lwd=3,col=5)
R_sq_plus1[i]=cor(combined_noinhibitor_nonlinearminus7[[i]]$plus1,plus1_3[which(is.finite(wt_3[,i])),i])^2
legend('bottomright',legend=bquote(R^2 == .(round(R_sq_plus1[i],2))), bty='n')
# plotting -7
plot(time,minus7_3[,i],main=paste('w/o NU7441:',colnames(wt_3)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='-7/(intact+mutated) (%)',xlab='time (h)',col=6);
lines(time[which(is.finite(wt_3[,i]))],combined_noinhibitor_nonlinearminus7[[i]]$minus7*100,lwd=3,col=6)
R_sq_minus7[i]=cor(combined_noinhibitor_nonlinearminus7[[i]]$minus7,minus7_3[which(is.finite(wt_3[,i])),i])^2
legend('bottomright',legend=bquote(R^2 == .(round(R_sq_minus7[i],2))), bty='n')
# plot -7 repair activity
plot(main=paste('w/o NU7441:',colnames(minus7_3)[i]),time, ((log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7b-log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7a)/max(time)*time+log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7a),type='l',lwd=2,xlab='time (h)', ylab=expression('k'['-7']*' (h'^-1*')'), ylim=range(c(((log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7b-log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7a)/max(time)*time+log(2)/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7a)),0))
legend('topleft',legend=bquote('activity'[0]==.(round(combined_noinhibitor_nonlinearminus7[[i]]$hlmn7b/combined_noinhibitor_nonlinearminus7[[i]]$hlmn7a*100,1))*'%'),bty='n')
# plotting broken
plot(seq(0,max(time),0.1),main=paste('w/o NU7441:',colnames(wt_3)[i]),combined_noinhibitor_nonlinearminus7[[i]]$broken*100,lwd=3,col=4,type='l',ylab='broken (%)',xlab='time (h)')
abline(v=combined_noinhibitor_nonlinearminus7[[i]]$peakBro,col=4,lwd=3,lty=2)
abline(h=0,col=4,lwd=3,lty=2)
legend('topright',legend=bquote(tau == .(combined_noinhibitor_nonlinearminus7[[i]]$peakBro)~' h '), bty='n')
#plot rates
bplt=barplot(main=paste('w/o NU7441:',colnames(wt_3)[i]),c(kcmax,kp,km,kpl1,kmn7a,kmn7b),
col=c(4,1,2,5,6,7),names.arg=
c(expression('k'['c']*'max'),
expression('k'['p']),
expression('k'['m']),
expression('k'['+1']),
expression('k'['-7,t=0h']),
expression('k'['-7,t=60h'])
),horiz=TRUE,las=2,xlab=expression('h'^-1));
rg=range(c(kcmax,kp,km,kpl1,kmn7a,kmn7b));
text(mean(rg),bplt[1,1],bquote(.(round(kcmax/10^floor(log10(kcmax)),1)) *'x 10' ^ .(floor(log10(kcmax)))),col=3,cex=1.5)
text(mean(rg),bplt[2,1],bquote(.(round(kp/10^floor(log10(kp)),1)) *'x 10' ^ .(floor(log10(kp)))),col=3,cex=1.5)
text(mean(rg),bplt[3,1],bquote(.(round(km/10^floor(log10(km)),1)) *'x 10' ^ .(floor(log10(km)))),col=3,cex=1.5)
text(mean(rg),bplt[4,1],bquote(.(round(kpl1/10^floor(log10(kpl1)),1)) *'x 10' ^ .(floor(log10(kpl1)))),col=3,cex=1.5)
text(mean(rg),bplt[5,1],bquote(.(round(kmn7a/10^floor(log10(kmn7a)),1)) *'x 10' ^ .(floor(log10(kmn7a)))),col=3,cex=1.5)
text(mean(rg),bplt[6,1],bquote(.(round(kmn7b/10^floor(log10(kmn7b)),1)) *'x 10' ^ .(floor(log10(kmn7b)))),col=3,cex=1.5)
}
print(R_sq)## [1] 0.9964123 0.9961313 0.9975336 0.9972901 0.9945901 0.9958532 0.9848980print(mean(R_sq))## [1] 0.9946727print(sd(R_sq))## [1] 0.004418441print(R_sq_plus1)## [1] 0.9856331 0.9928226 0.9972442 0.9934566 0.9948345 0.9961438 0.9932359print(mean(R_sq_plus1))## [1] 0.9933387print(sd(R_sq_plus1))## [1] 0.003764455print(R_sq_minus7)## [1] 0.9894183 0.9955700 0.9912286 0.9832485 0.9939682 0.9949544 0.9898965print(mean(R_sq_minus7))## [1] 0.9911835print(sd(R_sq_minus7))## [1] 0.004261329#################################################################
# fitting for withinhibitor
#################################################################
R_sq_i=NA
R_sq_i_plus1=NA
R_sq_i_minus7=NA
combined_withinhibitor_nonlinearminus7 = rep(list(NULL),ncol(wt_4));
for (i in 1:ncol(wt_4)){
combined_withinhibitor_nonlinearminus7[[i]]=ZLWB2fit_MutantsDefaultMinus7ab(time,wt_4[,i],indel_4[,i],plus1_4[,i],minus7_4[,i]);
AA = UntransfectedFitting(time,wt_4[,i],indel_4[,i]);
# data
plot(time,wt_4[,i],main=paste(colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
# untransfected
plot(time,wt_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)');
lines(seq(0,60,0.01),(1-AA[1]/(1 + exp(-AA[2] * (seq(0,60,0.01)-AA[3]))))*100,lwd=3);
legend('topright',paste('U =',round(100-AA[1]*100),'%'),bty='n')
# fitting intact
plot(time,wt_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='intact/(intact+mutated) (%)',xlab='time (h)',col=2);
lines(time[which(is.finite(wt_4[,i]))],combined_withinhibitor_nonlinearminus7[[i]]$intact*100,lwd=3,col=2)
R_sq_i[i]=cor(combined_withinhibitor_nonlinearminus7[[i]]$intact,wt_4[which(is.finite(wt_4[,i])),i])^2;
kcmax= log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlc;
kp= log(2)/(combined_withinhibitor_nonlinearminus7[[i]]$hlm/combined_withinhibitor_nonlinearminus7[[i]]$perfectF);
km=log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlm;
kpl1=log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlpl1;
kmn7a=log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7a;
kmn7b=log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7b;
legend('bottomleft',legend=bquote(R^2 == .(round(R_sq[i],2))), bty='n')
# plotting mutants
plot(time,indel_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='mutated/(intact+mutated) (%)',xlab='time (h)',col=3);
lines(time[which(is.finite(wt_4[,i]))],combined_withinhibitor_nonlinearminus7[[i]]$mutant*100,lwd=3,col=3)
R_sq_i[i]=cor(combined_withinhibitor_nonlinearminus7[[i]]$mutant,indel_4[which(is.finite(wt_4[,i])),i])^2
legend('bottomright',legend=bquote(R^2 == .(R_sq_i[i])), bty='n')
# plotting +1
plot(time,plus1_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='+1/(intact+mutated) (%)',xlab='time (h)',col=5);
lines(time[which(is.finite(wt_4[,i]))],combined_withinhibitor_nonlinearminus7[[i]]$plus1*100,lwd=3,col=5)
R_sq_i_plus1[i]=cor(combined_withinhibitor_nonlinearminus7[[i]]$plus1,plus1_4[which(is.finite(wt_4[,i])),i])^2
legend('bottomright',legend=bquote(R^2 == .(round(R_sq_i_plus1[i],2))), bty='n')
# plotting -7
plot(time,minus7_4[,i],main=paste('with NU7441:',colnames(wt_4)[i]),ylim=c(0,100),pch=16,cex=1.5,ylab='-7/(intact+mutated) (%)',xlab='time (h)',col=6);
lines(time[which(is.finite(wt_4[,i]))],combined_withinhibitor_nonlinearminus7[[i]]$minus7*100,lwd=3,col=6)
R_sq_i_minus7[i]=cor(combined_withinhibitor_nonlinearminus7[[i]]$minus7,minus7_4[which(is.finite(wt_4[,i])),i])^2
legend('bottomright',legend=bquote(R^2 == .(round(R_sq_i_minus7[i],2))), bty='n')
# plot -7 repair activity
plot(main=paste('w/o NU7441:',colnames(minus7_3)[i]),time, ((log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7b-log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7a)/max(time)*time+log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7a),type='l',lwd=2,xlab='time (h)', ylab=expression('k'['-7']*' (h'^-1*')'), ylim=range(c(((log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7b-log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7a)/max(time)*time+log(2)/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7a)),0))
legend('topleft',legend=bquote('activity'[0]==.(round(combined_withinhibitor_nonlinearminus7[[i]]$hlmn7b/combined_withinhibitor_nonlinearminus7[[i]]$hlmn7a*100,1))*'%'),bty='n')
# plotting broken
plot(seq(0,max(time),0.1),main=paste('with NU7441:',colnames(wt_4)[i]),combined_withinhibitor_nonlinearminus7[[i]]$broken*100,lwd=3,col=4,type='l',ylab='broken (%)',xlab='time (h)')
abline(v=combined_withinhibitor_nonlinearminus7[[i]]$peakBro,col=4,lwd=3,lty=2)
abline(h=0,col=4,lwd=3,lty=2)
legend('topright',legend=bquote(tau == .(combined_withinhibitor_nonlinearminus7[[i]]$peakBro)~' h '), bty='n')
#plot rates
bplt=barplot(main=paste('with NU7441:',colnames(wt_4)[i]),c(kcmax,kp,km,kpl1,kmn7a,kmn7b),
col=c(4,1,2,5,6,7),names.arg=
c(expression('k'['c']*'max'),
expression('k'['p']),
expression('k'['m']),
expression('k'['+1']),
expression('k'['-7,t=0h']),
expression('k'['-7,t=60h'])
),horiz=TRUE,las=2,xlab=expression('h'^-1));
rg=range(c(kcmax,kp,km,kpl1,kmn7a,kmn7b));
text(mean(rg),bplt[1,1],bquote(.(round(kcmax/10^floor(log10(kcmax)),1)) *'x 10' ^ .(floor(log10(kcmax)))),col=3,cex=1.5)
text(mean(rg),bplt[2,1],bquote(.(round(kp/10^floor(log10(kp)),1)) *'x 10' ^ .(floor(log10(kp)))),col=3,cex=1.5)
text(mean(rg),bplt[3,1],bquote(.(round(km/10^floor(log10(km)),1)) *'x 10' ^ .(floor(log10(km)))),col=3,cex=1.5)
text(mean(rg),bplt[4,1],bquote(.(round(kpl1/10^floor(log10(kpl1)),1)) *'x 10' ^ .(floor(log10(kpl1)))),col=3,cex=1.5)
text(mean(rg),bplt[5,1],bquote(.(round(kmn7a/10^floor(log10(kmn7a)),1)) *'x 10' ^ .(floor(log10(kmn7a)))),col=3,cex=1.5)
text(mean(rg),bplt[6,1],bquote(.(round(kmn7b/10^floor(log10(kmn7b)),1)) *'x 10' ^ .(floor(log10(kmn7b)))),col=3,cex=1.5)
}
print(R_sq_i)## [1] 0.9958576 0.9934274 0.9955111 0.9917787print(mean(R_sq_i))## [1] 0.9941437print(sd(R_sq_i))## [1] 0.001907341print(R_sq_i_plus1)## [1] 0.9849910 0.9635727 0.9988991 0.9670858print(mean(R_sq_i_plus1))## [1] 0.9786372print(sd(R_sq_i_plus1))## [1] 0.01644474print(R_sq_i_minus7)## [1] 0.9891092 0.9830643 0.9893449 0.9863250print(mean(R_sq_i_minus7))## [1] 0.9869609print(sd(R_sq_i_minus7))## [1] 0.002937504Compare the accerlating model (combined model) with the constant model
1: R_sq comparison
2: F-statistic: invididual series
3: without and with inhibitor
rg=c(0.8,1);
# without inhibitor
beeswarm(main='w/o NU7441',list(R2calculator(indel_1,sapply(noinhibitor_total_individual_series,function(x){x$mutant})),R2calculator(indel_3,sapply(noinhibitor_total_individual_series_split,function(x){x$mutant})),R2calculator(plus1_3,sapply(noinhibitor_total_individual_series_split,function(x){x$plus1})),R2calculator(minus7_3,sapply(noinhibitor_total_individual_series_split,function(x){x$minus7})),R2calculator(minus7_3,sapply(combined_noinhibitor_nonlinearminus7,function(x){x$minus7})))
,pch=c(16,16,17,18,15),cex=1.5,col=c(6,3,3,3,4),labels = c("total", "total","+1","-7",'-7'),ylab=expression('R'^2),ylim=rg)
legend('bottomleft',c(paste(round(mean(R2calculator(indel_1,sapply(noinhibitor_total_individual_series,function(x){x$mutant}))),2),'+/-',round(sd(R2calculator(indel_1,sapply(noinhibitor_total_individual_series,function(x){x$mutant}))),2))
,paste(round(mean(R2calculator(indel_3,sapply(noinhibitor_total_individual_series_split,function(x){x$mutant}))),2),'+/-',round(sd(R2calculator(indel_3,sapply(noinhibitor_total_individual_series_split,function(x){x$mutant}))),2)),
paste(round(mean(R2calculator(plus1_3,sapply(noinhibitor_total_individual_series_split,function(x){x$plus1}))),2),'+/-',round(sd(R2calculator(plus1_3,sapply(noinhibitor_total_individual_series_split,function(x){x$plus1}))),2)),
paste(round(mean(R2calculator(minus7_3,sapply(noinhibitor_total_individual_series_split,function(x){x$minus7}))),2),'+/-',round(sd(R2calculator(minus7_3,sapply(noinhibitor_total_individual_series_split,function(x){x$minus7}))),2)),
paste(round(mean(R2calculator(minus7_3,sapply(combined_noinhibitor_nonlinearminus7,function(x){x$minus7}))),2),'+/-',round(sd(R2calculator(minus7_3,sapply(combined_noinhibitor_nonlinearminus7,function(x){x$minus7}))),2))
),pch=c(16,16,17,18,15),col=c(6,3,3,3,4),bty='n')
legend('bottomright',c('combined','constant','accelerating'),text.col=c(6,3,4),bty='n')
# with inhibitor
beeswarm(main='with NU7441',list(R2calculator(indel_2,sapply(withinhibitor_total_individual_series,function(x){x$mutant})),R2calculator(indel_4,sapply(withinhibitor_total_individual_series_split,function(x){x$mutant})),R2calculator(plus1_4,sapply(withinhibitor_total_individual_series_split,function(x){x$plus1})),R2calculator(minus7_4,sapply(withinhibitor_total_individual_series_split,function(x){x$minus7})),R2calculator(minus7_4,sapply(combined_withinhibitor_nonlinearminus7,function(x){x$minus7})))
,pch=c(16,16,17,18,15),cex=1.5,col=c(6,3,3,3,4),labels = c("total", "total","+1","-7",'-7'),ylab=expression('R'^2),ylim=rg)
legend('bottomleft',c(paste(round(mean(R2calculator(indel_2,sapply(withinhibitor_total_individual_series,function(x){x$mutant}))),2),'+/-',round(sd(R2calculator(indel_2,sapply(withinhibitor_total_individual_series,function(x){x$mutant}))),2))
,paste(round(mean(R2calculator(indel_4,sapply(withinhibitor_total_individual_series_split,function(x){x$mutant}))),2),'+/-',round(sd(R2calculator(indel_4,sapply(withinhibitor_total_individual_series_split,function(x){x$mutant}))),2)),
paste(round(mean(R2calculator(plus1_4,sapply(withinhibitor_total_individual_series_split,function(x){x$plus1}))),2),'+/-',round(sd(R2calculator(plus1_4,sapply(withinhibitor_total_individual_series_split,function(x){x$plus1}))),2)),
paste(round(mean(R2calculator(minus7_4,sapply(withinhibitor_total_individual_series_split,function(x){x$minus7}))),2),'+/-',round(sd(R2calculator(minus7_4,sapply(withinhibitor_total_individual_series_split,function(x){x$minus7}))),2)),
paste(round(mean(R2calculator(minus7_4,sapply(combined_withinhibitor_nonlinearminus7,function(x){x$minus7}))),2),'+/-',round(sd(R2calculator(minus7_4,sapply(combined_withinhibitor_nonlinearminus7,function(x){x$minus7}))),2))
),pch=c(16,16,17,18,15),col=c(6,3,3,3,4),bty='n')
legend('bottomright',c('combined','constant','accelerating'),text.col=c(6,3,4),bty='n')
#F statistic
#################################################################
# extract time point information
time = as.numeric(row.names(wildtype_tot_LBR2));
#### no inhibitor
### individual series
df2 = min(apply(minus7_3,2,function(x){sum(as.numeric(!is.na(x)))-1}));
# residual sum square for constant model
conresisq = colSums((apply(minus7_3,2,function(x){x[!is.na(x)]})/100-sapply(noinhibitor_total_individual_series_split,function(x){x$minus7}))^2);
# residual sum square for accelerating model
accresisq = colSums((apply(minus7_3,2,function(x){x[!is.na(x)]})/100-sapply(combined_noinhibitor_nonlinearminus7,function(x){x$minus7}))^2);
rg = range(c(log2(conresisq),log2(accresisq)));
plot(main='w/o NU7441',log2(conresisq),log2(accresisq),xlim=rg,ylim=rg,pch=16,col=4,cex=2,xlab=bquote('log'[2]*'(sum of resid'^2*'): constant'),ylab=bquote('log'[2]*'(sum of resid'^2*'): accelerating'));
abline(-log2(qf(0.95,df2,df2-1)),1,lwd=2,lty=2,col=3)
abline(-log2(qf(0.5,df2,df2-1)),1,lwd=2,lty=2)
abline(log2(qf(0.5,df2-1,df2)),1,lwd=2,lty=2)
abline(log2(qf(0.95,df2-1,df2)),1,lwd=2,lty=2,col=6)
text(mean(rg)+log2(qf(0.95,df2,df2-1))/2,mean(rg)-log2(qf(0.95,df2,df2-1))/2,'p=0.05',pos=4,col=3)
text(mean(rg)-log2(qf(0.95,df2-1,df2))/2,mean(rg)+log2(qf(0.95,df2-1,df2))/2,'p=0.05',pos=2,col=6)
text(mean(rg),mean(rg),'p=0.5')
legend('topleft','constant model is better',bty='n',text.col=6)
legend('bottomright','accelerating model is better',bty='n',text.col=3)
#### with inhibitor
### individual series
df2 = min(apply(minus7_4,2,function(x){sum(as.numeric(!is.na(x)))-1}));
# residual sum square for constant model
conresisq = colSums((apply(minus7_4,2,function(x){x[!is.na(x)]})/100-sapply(withinhibitor_total_individual_series_split,function(x){x$minus7}))^2);
# residual sum square for accelerating model
accresisq = colSums((apply(minus7_4,2,function(x){x[!is.na(x)]})/100-sapply(combined_withinhibitor_nonlinearminus7,function(x){x$minus7}))^2);
rg = range(c(log2(conresisq),log2(accresisq)));
plot(main='with NU7441',log2(conresisq),log2(accresisq),xlim=rg,ylim=rg,pch=16,col=2,cex=2,xlab=bquote('log'[2]*'(sum of resid'^2*'): constant'),ylab=bquote('log'[2]*'(sum of resid'^2*'): accelerating'));
abline(-log2(qf(0.95,df2,df2-1)),1,lwd=2,lty=2,col=3)
abline(-log2(qf(0.5,df2,df2-1)),1,lwd=2,lty=2)
abline(log2(qf(0.5,df2-1,df2)),1,lwd=2,lty=2)
abline(log2(qf(0.95,df2-1,df2)),1,lwd=2,lty=2,col=6)
text(mean(rg)+log2(qf(0.95,df2,df2-1))/2,mean(rg)-log2(qf(0.95,df2,df2-1))/2,'p=0.05',pos=4,col=3)
text(mean(rg)-log2(qf(0.95,df2-1,df2))/2,mean(rg)+log2(qf(0.95,df2-1,df2))/2,'p=0.05',pos=2,col=6)
text(mean(rg),mean(rg),'p=0.5')
legend('topleft','constant model is better',bty='n',text.col=6)
legend('bottomright','accelerating model is better',bty='n',text.col=3)
Compare combinated mutation model and individual mutation model
split into without and with NU7441
Compare common values for hlc, hlp, hlm and peakBro
####without inhibitor
# hlc
beeswarm(main='w/o NU7441',list(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlc})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['c']*'max'*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlc}),method='spearman'),2))),bty='n')
# hlp
beeswarm(main='w/o NU7441',list(log(2)/(sapply(noinhibitor_total_individual_series,function(x){x$hlm})/sapply(noinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$perfectF}))),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['p']*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/(sapply(noinhibitor_total_individual_series,function(x){x$hlm})/sapply(noinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$perfectF})),method='spearman'),2))),bty='n')
# hlm
beeswarm(main='w/o NU7441',list(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['m']*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/sapply(noinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm}),method='spearman'),2))),bty='n')
# peakBro
beeswarm(main='w/o NU7441',list(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}),sapply(combined_noinhibitor_nonlinearminus7,function(x){x$peakBro})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression(tau *' (h)'))
legend('topleft',legend=bquote(rho==.(round(cor(sapply(noinhibitor_total_individual_series,function(x){x$peakBro}),sapply(combined_noinhibitor_nonlinearminus7,function(x){x$peakBro}),method='spearman'),2))),bty='n')
####without inhibitor
# hlc
beeswarm(main='with NU7441',list(log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlc})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['c']*'max'*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlc}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlc}),method='spearman'),2))),bty='n')
# hlp
beeswarm(main='with NU7441',list(log(2)/(sapply(withinhibitor_total_individual_series,function(x){x$hlm})/sapply(withinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$perfectF}))),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['p']*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/(sapply(withinhibitor_total_individual_series,function(x){x$hlm})/sapply(withinhibitor_total_individual_series,function(x){x$perfectF})),log(2)/(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$perfectF})),method='spearman'),2))),bty='n')
# hlm
beeswarm(main='with NU7441',list(log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression('k'['m']*' (h'^-1*')'))
legend('topleft',legend=bquote(rho==.(round(cor(log(2)/sapply(withinhibitor_total_individual_series,function(x){x$hlm}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm}),method='spearman'),2))),bty='n')
# peakBro
beeswarm(main='with NU7441',list(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}),sapply(combined_withinhibitor_nonlinearminus7,function(x){x$peakBro})),pch=c(16,16),cex=1.5,col=c(6,3),labels = c("fitting comb.mut", "fitting indv. mut"),ylab=expression(tau *' (h)'))
legend('topleft',legend=bquote(rho==.(round(cor(sapply(withinhibitor_total_individual_series,function(x){x$peakBro}),sapply(combined_withinhibitor_nonlinearminus7,function(x){x$peakBro}),method='spearman'),2))),bty='n')
Collect individual data series and represent parameters, +1 and -7a, -7b, accelerating model (combined model)
library(beeswarm);
# hlc
beeswarm(list(log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlc}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlc})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['c']*'max'*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlc})),1),'±',round(sd(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlc})),1)),paste(round(mean(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlc})),1),'+/-',round(sd(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlc})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlc});
d2 = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlc});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
text(lgd$text$x[2]+(lgd$text$x[2]-lgd$text$x[1]),lgd$text$y[2]
+(lgd$text$y[2]-lgd$text$y[1]),paste('p =',round(wilcox.test(d1,d2)$p.value,2)))
# hlp
beeswarm(list(log(2)/(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$perfectF})),log(2)/(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$perfectF}))),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['p']*' (h'^-1*')'));
lgd = legend('bottomright',c(paste(round(mean(log(2)/(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$perfectF}))),2),'+/-',round(sd(log(2)/(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$perfectF}))),2)),paste(round(mean(log(2)/(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$perfectF}))),2),'+/-',round(sd(log(2)/(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$perfectF}))),2))),text.col=c(0,0),bty='n')
d1 = (log(2)/(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$perfectF})));
d2 = (log(2)/(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$perfectF})));
text(lgd$text$x[1]-(lgd$text$x[2]-lgd$text$x[1]),lgd$text$y[1]-(lgd$text$y[2]-lgd$text$y[1]),bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
text(lgd$text$x[2],lgd$text$y[2],paste('p =',round(wilcox.test(d1,d2)$p.value,2)))
# hlm
beeswarm(list(log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['m']*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})),1),'±',round(sd(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})),1)),paste(round(mean(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})),1),'+/-',round(sd(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm});
d2 = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
text(lgd$text$x[2]+(lgd$text$x[2]-lgd$text$x[1]),lgd$text$y[2]
+(lgd$text$y[2]-lgd$text$y[1]),paste('p =',round(wilcox.test(d1,d2)$p.value,2)))
# hlpl1
beeswarm(list(log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlpl1}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlpl1})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['+1']*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlpl1})),1),'±',round(sd(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlpl1})),1)),paste(round(mean(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlpl1})),1),'+/-',round(sd(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlpl1})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlpl1});
d2 = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlpl1});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
text(lgd$text$x[2]+(lgd$text$x[2]-lgd$text$x[1]),lgd$text$y[2]
+(lgd$text$y[2]-lgd$text$y[1]),paste('p =',round(wilcox.test(d1,d2)$p.value,2)))
# hlmn7 a
beeswarm(list(log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7a}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7a})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['-7,t=0h']*' (h'^-1*')'))
lgd = legend('bottomright',c(paste(round(mean(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1),'±',round(sd(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1)),paste(round(mean(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1),'±',round(sd(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7a});
d2 = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7a});
text(lgd$text$x[1]-(lgd$text$x[2]-lgd$text$x[1]),lgd$text$y[1]-(lgd$text$y[2]-lgd$text$y[1]),bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
text(lgd$text$x[2],lgd$text$y[2],paste('p =',round(wilcox.test(d1,d2)$p.value,2)))
# hlmn7 b
beeswarm(list(log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b}),log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=expression('k'['-7,t=60h']*' (h'^-1*')'))
lgd = legend('topright',c(paste(round(mean(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1),'±',round(sd(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1)),paste(round(mean(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1),'±',round(sd(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1))),text.col=c(0,0),bty='n');
d1 = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b});
d2 = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b});
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[2],lgd$text$y[2],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
text(lgd$text$x[2]+(lgd$text$x[2]-lgd$text$x[1]),lgd$text$y[2]
+(lgd$text$y[2]-lgd$text$y[1]),paste('p =',round(wilcox.test(d1,d2)$p.value,2)))
# activity zero
beeswarm(list(100*sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7a}),100*sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7a})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=bquote('a'[0]*' (%)'))
d1 = 100*sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7a});
d2 = 100*sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7a});
lgd = legend('bottomright',c(paste(round(mean(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1),'±',round(sd(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1)),paste(round(mean(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1),'±',round(sd(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b})),1))),text.col=c(0,0),bty='n');
text(lgd$text$x[1]-(lgd$text$x[2]-lgd$text$x[1]),lgd$text$y[1]-(lgd$text$y[2]-lgd$text$y[1]),bquote('('*.(round(mean(d1)/10^(floor(log10(mean(d1)))),1)) *'±'* .(round(sd(d1)/10^(floor(log10(mean(d1)))),1))*') x 10' ^ .(floor(log10(mean(d1))))),col=4)
text(lgd$text$x[1],lgd$text$y[1],bquote('('*.(round(mean(d2)/10^(floor(log10(mean(d2)))),1)) *'±'* .(round(sd(d2)/10^(floor(log10(mean(d2)))),1))*') x 10' ^ .(floor(log10(mean(d2))))),col=2)
text(lgd$text$x[2],lgd$text$y[2],paste('p =',round(wilcox.test(d1,d2)$p.value,2)))
# peakBro
beeswarm(list(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$peakBro}),sapply(combined_withinhibitor_nonlinearminus7,function(x){x$peakBro})),pch=16,cex=1.5,col=c(4,2),labels = c("w/o NU7441", "with NU7441"),ylab=bquote(tau * ' (h)' ))
d1 = sapply(combined_noinhibitor_nonlinearminus7,function(x){x$peakBro})
d2 = sapply(combined_withinhibitor_nonlinearminus7,function(x){x$peakBro})
lgd = legend('bottomright',c(paste(round(mean(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$peakBro})),1),'±',round(sd(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$peakBro})),1)),paste(round(mean(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$peakBro})),1),'±',round(sd(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$peakBro})),1))),text.col=c(0,0),bty='n');
text(lgd$text$x[1]-(lgd$text$x[2]-lgd$text$x[1]),lgd$text$y[1]-(lgd$text$y[2]-lgd$text$y[1]),paste(round(mean(d1),1),'±',round(sd(d1),1)),col=4)
text(lgd$text$x[1],lgd$text$y[1],paste(round(mean(d2),1),'±',round(sd(d2),1)),col=2)
text(lgd$text$x[2],lgd$text$y[2],paste('p =',round(wilcox.test(d1,d2)$p.value,2)))
Accumulating all the rates together, accelerating model (combined model)
real rates
arbitrary units for relative effects
library(beeswarm);
rates_AM = rep(list(NULL),6*2);
# hlc
rates_AM[[1]] = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlc});
rates_AM[[2]] = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlc});
# hlp
rates_AM[[3]] = (log(2)/(sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$perfectF})));
rates_AM[[4]] = (log(2)/(sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm})/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$perfectF})));
# hlm
rates_AM[[5]] = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlm});
rates_AM[[6]] = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlm});
# hlpl1
rates_AM[[7]] = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlpl1});
rates_AM[[8]] = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlpl1});
# hlmn7 a
rates_AM[[9]] = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7a});
rates_AM[[10]] = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7a});
# hlmn7 b
rates_AM[[11]] = log(2)/sapply(combined_noinhibitor_nonlinearminus7,function(x){x$hlmn7b});
rates_AM[[12]] = log(2)/sapply(combined_withinhibitor_nonlinearminus7,function(x){x$hlmn7b});
bm=beeswarm(rates_AM,pch=16,cex=1,col=c(4,2),las=2,labels='',ylab=expression('h'^-1),corral='wrap')
axis(1,at=seq(1.5,11.5,2),labels=c(expression('k'['c']*'max'),expression('k'['p']),expression('k'['m']),expression('k'['+1']),expression('k'['-7,t=0h']),expression('k'['-7,t=60h'])))
legend("topright",
legend = c("w/o NU7441", "with NU7441"),
text.col = c(4, 2),bty='n')
SessionInfo
sessionInfo()## R version 3.3.2 (2016-10-31)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
##
## locale:
## [1] LC_COLLATE=Dutch_Netherlands.1252 LC_CTYPE=Dutch_Netherlands.1252
## [3] LC_MONETARY=Dutch_Netherlands.1252 LC_NUMERIC=C
## [5] LC_TIME=Dutch_Netherlands.1252
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Hmisc_4.0-2 ggplot2_2.2.1 Formula_1.2-1
## [4] survival_2.40-1 lattice_0.20-34 beeswarm_0.2.3
## [7] colorspace_1.3-2 truncnorm_1.0-7 Biostrings_2.42.1
## [10] XVector_0.14.0 IRanges_2.8.1 S4Vectors_0.12.1
## [13] BiocGenerics_0.20.0 FME_1.3.5 coda_0.19-1
## [16] rootSolve_1.7 minpack.lm_1.2-1 deSolve_1.14
## [19] plyr_1.8.4
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.8 RColorBrewer_1.1-2 base64enc_0.1-3
## [4] tools_3.3.2 zlibbioc_1.20.0 rpart_4.1-10
## [7] digest_0.6.11 checkmate_1.8.2 htmlTable_1.8
## [10] evaluate_0.10 tibble_1.2 gtable_0.2.0
## [13] Matrix_1.2-7.1 yaml_2.1.14 gridExtra_2.2.1
## [16] cluster_2.0.5 stringr_1.1.0 knitr_1.20
## [19] nnet_7.3-12 rprojroot_1.2 grid_3.3.2
## [22] data.table_1.10.0 foreign_0.8-67 rmarkdown_1.5
## [25] latticeExtra_0.6-28 minqa_1.2.4 magrittr_1.5
## [28] backports_1.0.5 scales_0.4.1 htmltools_0.3.5
## [31] MASS_7.3-45 splines_3.3.2 assertthat_0.1
## [34] stringi_1.1.2 acepack_1.4.1 lazyeval_0.2.0
## [37] munsell_0.4.3